Overview

Dataset statistics

Number of variables63
Number of observations2261
Missing cells6647
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory497.2 B

Variable types

Numeric8
Categorical52
Boolean3

Alerts

country has a high cardinality: 105 distinct values High cardinality
currency_desc has a high cardinality: 73 distinct values High cardinality
currency_symbol has a high cardinality: 73 distinct values High cardinality
database_desire_next_year has a high cardinality: 576 distinct values High cardinality
database_worked_with has a high cardinality: 594 distinct values High cardinality
dev_type has a high cardinality: 995 distinct values High cardinality
ethnicity has a high cardinality: 51 distinct values High cardinality
job_factors has a high cardinality: 180 distinct values High cardinality
language_desire_next_year has a high cardinality: 1335 distinct values High cardinality
language_worked_with has a high cardinality: 1271 distinct values High cardinality
misc_tech_desire_next_year has a high cardinality: 783 distinct values High cardinality
misc_tech_worked_with has a high cardinality: 578 distinct values High cardinality
new_collab_tools_desire_next_year has a high cardinality: 402 distinct values High cardinality
new_collab_tools_worked_with has a high cardinality: 406 distinct values High cardinality
new_job_hunt has a high cardinality: 697 distinct values High cardinality
new_job_hunt_research has a high cardinality: 62 distinct values High cardinality
new_purchase_research has a high cardinality: 56 distinct values High cardinality
new_stuck has a high cardinality: 215 distinct values High cardinality
platform_desire_next_year has a high cardinality: 948 distinct values High cardinality
platform_worked_with has a high cardinality: 897 distinct values High cardinality
webframe_desire_next_year has a high cardinality: 435 distinct values High cardinality
webframe_worked_with has a high cardinality: 502 distinct values High cardinality
age is highly correlated with years_code and 1 other fieldsHigh correlation
years_code is highly correlated with age and 1 other fieldsHigh correlation
years_code_pro is highly correlated with age and 1 other fieldsHigh correlation
age is highly correlated with years_code and 1 other fieldsHigh correlation
years_code is highly correlated with age and 1 other fieldsHigh correlation
years_code_pro is highly correlated with age and 1 other fieldsHigh correlation
age is highly correlated with years_code and 1 other fieldsHigh correlation
years_code is highly correlated with age and 1 other fieldsHigh correlation
years_code_pro is highly correlated with age and 1 other fieldsHigh correlation
gender is highly correlated with transHigh correlation
so_part_freq is highly correlated with so_accountHigh correlation
currency_symbol is highly correlated with currency_descHigh correlation
trans is highly correlated with genderHigh correlation
currency_desc is highly correlated with currency_symbolHigh correlation
so_account is highly correlated with so_part_freqHigh correlation
age is highly correlated with years_code and 2 other fieldsHigh correlation
age_1st_code is highly correlated with age_first_code_cutHigh correlation
age_first_code_cut is highly correlated with age_1st_codeHigh correlation
comp_freq is highly correlated with converted_comp and 2 other fieldsHigh correlation
comp_total is highly correlated with currency_desc and 1 other fieldsHigh correlation
converted_comp is highly correlated with comp_freqHigh correlation
currency_desc is highly correlated with comp_freq and 4 other fieldsHigh correlation
currency_symbol is highly correlated with comp_freq and 4 other fieldsHigh correlation
employment is highly correlated with org_sizeHigh correlation
ethnicity is highly correlated with currency_desc and 4 other fieldsHigh correlation
gender is highly correlated with ethnicity and 2 other fieldsHigh correlation
new_purchase_research is highly correlated with ethnicity and 1 other fieldsHigh correlation
newso_sites is highly correlated with currency_desc and 2 other fieldsHigh correlation
org_size is highly correlated with employmentHigh correlation
sexuality is highly correlated with ethnicity and 2 other fieldsHigh correlation
so_account is highly correlated with so_commHigh correlation
so_comm is highly correlated with so_account and 1 other fieldsHigh correlation
so_part_freq is highly correlated with so_commHigh correlation
trans is highly correlated with gender and 1 other fieldsHigh correlation
years_code is highly correlated with age and 2 other fieldsHigh correlation
years_code_pro is highly correlated with age and 2 other fieldsHigh correlation
age_cat is highly correlated with age and 2 other fieldsHigh correlation
database_desire_next_year has 574 (25.4%) missing values Missing
database_worked_with has 327 (14.5%) missing values Missing
ethnicity has 169 (7.5%) missing values Missing
gender has 33 (1.5%) missing values Missing
job_factors has 28 (1.2%) missing values Missing
language_desire_next_year has 101 (4.5%) missing values Missing
misc_tech_desire_next_year has 307 (13.6%) missing values Missing
misc_tech_worked_with has 297 (13.1%) missing values Missing
new_collab_tools_desire_next_year has 238 (10.5%) missing values Missing
new_collab_tools_worked_with has 69 (3.1%) missing values Missing
new_dev_ops_impt has 78 (3.4%) missing values Missing
new_job_hunt has 70 (3.1%) missing values Missing
new_job_hunt_research has 112 (5.0%) missing values Missing
new_learn has 38 (1.7%) missing values Missing
new_off_topic has 106 (4.7%) missing values Missing
new_purchase_research has 781 (34.5%) missing values Missing
new_stuck has 29 (1.3%) missing values Missing
org_size has 38 (1.7%) missing values Missing
platform_desire_next_year has 196 (8.7%) missing values Missing
platform_worked_with has 91 (4.0%) missing values Missing
purchase_what has 208 (9.2%) missing values Missing
sexuality has 207 (9.2%) missing values Missing
so_part_freq has 377 (16.7%) missing values Missing
trans has 62 (2.7%) missing values Missing
undergrad_major has 110 (4.9%) missing values Missing
webframe_desire_next_year has 925 (40.9%) missing values Missing
webframe_worked_with has 809 (35.8%) missing values Missing
welcome_change has 62 (2.7%) missing values Missing
work_week_hrs has 42 (1.9%) missing values Missing
comp_total is highly skewed (γ1 = 26.99760535) Skewed
respondent has unique values Unique

Reproduction

Analysis started2022-08-24 11:10:25.531204
Analysis finished2022-08-24 11:11:19.748954
Duration54.22 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

respondent
Real number (ℝ≥0)

UNIQUE

Distinct2261
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28919.82795
Minimum36
Maximum62882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-08-24T16:41:19.905129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile2279
Q114033
median26119
Q346996
95-th percentile58954
Maximum62882
Range62846
Interquartile range (IQR)32963

Descriptive statistics

Standard deviation18404.37771
Coefficient of variation (CV)0.6363930569
Kurtosis-1.186327816
Mean28919.82795
Median Absolute Deviation (MAD)15885
Skewness0.228296954
Sum65387731
Variance338721119.1
MonotonicityStrictly increasing
2022-08-24T16:41:20.216936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
361
 
< 0.1%
356091
 
< 0.1%
353241
 
< 0.1%
353271
 
< 0.1%
354401
 
< 0.1%
354501
 
< 0.1%
355361
 
< 0.1%
355901
 
< 0.1%
357051
 
< 0.1%
352931
 
< 0.1%
Other values (2251)2251
99.6%
ValueCountFrequency (%)
361
< 0.1%
471
< 0.1%
691
< 0.1%
1251
< 0.1%
1471
< 0.1%
1521
< 0.1%
1661
< 0.1%
1701
< 0.1%
1871
< 0.1%
1961
< 0.1%
ValueCountFrequency (%)
628821
< 0.1%
628671
< 0.1%
628371
< 0.1%
628351
< 0.1%
628121
< 0.1%
627451
< 0.1%
627051
< 0.1%
626171
< 0.1%
626161
< 0.1%
626081
< 0.1%

main_branch
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
I am a developer by profession
1773 
I am not primarily a developer, but I write code sometimes as part of my work
488 

Length

Max length77
Median length30
Mean length40.14418399
Min length30

Characters and Unicode

Total characters90766
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI am not primarily a developer, but I write code sometimes as part of my work
2nd rowI am a developer by profession
3rd rowI am a developer by profession
4th rowI am not primarily a developer, but I write code sometimes as part of my work
5th rowI am not primarily a developer, but I write code sometimes as part of my work

Common Values

ValueCountFrequency (%)
I am a developer by profession1773
78.4%
I am not primarily a developer, but I write code sometimes as part of my work488
 
21.6%

Length

2022-08-24T16:41:20.533989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:20.815173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
i2749
14.9%
a2261
12.3%
developer2261
12.3%
am2261
12.3%
by1773
9.6%
profession1773
9.6%
sometimes488
 
2.6%
my488
 
2.6%
of488
 
2.6%
part488
 
2.6%
Other values (7)3416
18.5%

Most occurring characters

ValueCountFrequency (%)
16185
17.8%
e10508
11.6%
o8247
 
9.1%
r6474
 
7.1%
a5986
 
6.6%
p5010
 
5.5%
s5010
 
5.5%
m4213
 
4.6%
i3725
 
4.1%
I2749
 
3.0%
Other values (13)22659
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter71344
78.6%
Space Separator16185
 
17.8%
Uppercase Letter2749
 
3.0%
Other Punctuation488
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10508
14.7%
o8247
11.6%
r6474
 
9.1%
a5986
 
8.4%
p5010
 
7.0%
s5010
 
7.0%
m4213
 
5.9%
i3725
 
5.2%
d2749
 
3.9%
l2749
 
3.9%
Other values (10)16673
23.4%
Space Separator
ValueCountFrequency (%)
16185
100.0%
Uppercase Letter
ValueCountFrequency (%)
I2749
100.0%
Other Punctuation
ValueCountFrequency (%)
,488
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin74093
81.6%
Common16673
 
18.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10508
14.2%
o8247
11.1%
r6474
 
8.7%
a5986
 
8.1%
p5010
 
6.8%
s5010
 
6.8%
m4213
 
5.7%
i3725
 
5.0%
I2749
 
3.7%
d2749
 
3.7%
Other values (11)19422
26.2%
Common
ValueCountFrequency (%)
16185
97.1%
,488
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII90766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16185
17.8%
e10508
11.6%
o8247
 
9.1%
r6474
 
7.1%
a5986
 
6.6%
p5010
 
5.5%
s5010
 
5.5%
m4213
 
4.6%
i3725
 
4.1%
I2749
 
3.0%
Other values (13)22659
25.0%

hobbyist
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
True
1833 
False
428 
ValueCountFrequency (%)
True1833
81.1%
False428
 
18.9%
2022-08-24T16:41:21.022008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.90712074
Minimum16
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-08-24T16:41:21.237483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q126
median30
Q336
95-th percentile48
Maximum85
Range69
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.225540178
Coefficient of variation (CV)0.2577963786
Kurtosis2.562049469
Mean31.90712074
Median Absolute Deviation (MAD)5
Skewness1.343034715
Sum72142
Variance67.65951122
MonotonicityNot monotonic
2022-08-24T16:41:21.424937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26161
 
7.1%
30160
 
7.1%
25152
 
6.7%
28146
 
6.5%
29128
 
5.7%
27118
 
5.2%
31111
 
4.9%
24109
 
4.8%
23108
 
4.8%
3298
 
4.3%
Other values (43)970
42.9%
ValueCountFrequency (%)
161
 
< 0.1%
186
 
0.3%
196
 
0.3%
209
 
0.4%
2132
 
1.4%
2275
3.3%
23108
4.8%
24109
4.8%
25152
6.7%
26161
7.1%
ValueCountFrequency (%)
851
 
< 0.1%
741
 
< 0.1%
731
 
< 0.1%
721
 
< 0.1%
661
 
< 0.1%
641
 
< 0.1%
636
0.3%
621
 
< 0.1%
612
 
0.1%
601
 
< 0.1%

age_1st_code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct36
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.93321539
Minimum5
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-08-24T16:41:21.599402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q112
median15
Q318
95-th percentile23
Maximum45
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.767279494
Coefficient of variation (CV)0.3192399874
Kurtosis2.159029838
Mean14.93321539
Median Absolute Deviation (MAD)3
Skewness0.8216747217
Sum33764
Variance22.72695377
MonotonicityNot monotonic
2022-08-24T16:41:21.755616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
14230
10.2%
12220
9.7%
15206
 
9.1%
18189
 
8.4%
16183
 
8.1%
10178
 
7.9%
13148
 
6.5%
17133
 
5.9%
19120
 
5.3%
11107
 
4.7%
Other values (26)547
24.2%
ValueCountFrequency (%)
518
 
0.8%
627
 
1.2%
738
 
1.7%
884
 
3.7%
965
 
2.9%
10178
7.9%
11107
4.7%
12220
9.7%
13148
6.5%
14230
10.2%
ValueCountFrequency (%)
451
 
< 0.1%
421
 
< 0.1%
401
 
< 0.1%
381
 
< 0.1%
361
 
< 0.1%
351
 
< 0.1%
341
 
< 0.1%
334
0.2%
321
 
< 0.1%
312
0.1%

age_first_code_cut
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
adult
1376 
child
885 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11305
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadult
2nd rowchild
3rd rowchild
4th rowadult
5th rowadult

Common Values

ValueCountFrequency (%)
adult1376
60.9%
child885
39.1%

Length

2022-08-24T16:41:21.911802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:22.036800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
adult1376
60.9%
child885
39.1%

Most occurring characters

ValueCountFrequency (%)
d2261
20.0%
l2261
20.0%
a1376
12.2%
u1376
12.2%
t1376
12.2%
c885
 
7.8%
h885
 
7.8%
i885
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11305
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d2261
20.0%
l2261
20.0%
a1376
12.2%
u1376
12.2%
t1376
12.2%
c885
 
7.8%
h885
 
7.8%
i885
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Latin11305
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d2261
20.0%
l2261
20.0%
a1376
12.2%
u1376
12.2%
t1376
12.2%
c885
 
7.8%
h885
 
7.8%
i885
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII11305
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d2261
20.0%
l2261
20.0%
a1376
12.2%
u1376
12.2%
t1376
12.2%
c885
 
7.8%
h885
 
7.8%
i885
 
7.8%

comp_freq
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
Yearly
1338 
Monthly
861 
Weekly
 
62

Length

Max length7
Median length6
Mean length6.380804954
Min length6

Characters and Unicode

Total characters14427
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYearly
2nd rowYearly
3rd rowYearly
4th rowMonthly
5th rowYearly

Common Values

ValueCountFrequency (%)
Yearly1338
59.2%
Monthly861
38.1%
Weekly62
 
2.7%

Length

2022-08-24T16:41:22.210129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:22.444444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
yearly1338
59.2%
monthly861
38.1%
weekly62
 
2.7%

Most occurring characters

ValueCountFrequency (%)
l2261
15.7%
y2261
15.7%
e1462
10.1%
Y1338
9.3%
a1338
9.3%
r1338
9.3%
M861
 
6.0%
o861
 
6.0%
n861
 
6.0%
t861
 
6.0%
Other values (3)985
6.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12166
84.3%
Uppercase Letter2261
 
15.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l2261
18.6%
y2261
18.6%
e1462
12.0%
a1338
11.0%
r1338
11.0%
o861
 
7.1%
n861
 
7.1%
t861
 
7.1%
h861
 
7.1%
k62
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
Y1338
59.2%
M861
38.1%
W62
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin14427
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l2261
15.7%
y2261
15.7%
e1462
10.1%
Y1338
9.3%
a1338
9.3%
r1338
9.3%
M861
 
6.0%
o861
 
6.0%
n861
 
6.0%
t861
 
6.0%
Other values (3)985
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII14427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l2261
15.7%
y2261
15.7%
e1462
10.1%
Y1338
9.3%
a1338
9.3%
r1338
9.3%
M861
 
6.0%
o861
 
6.0%
n861
 
6.0%
t861
 
6.0%
Other values (3)985
6.8%

comp_total
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct555
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3238166.69
Minimum0
Maximum1900000000
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-08-24T16:41:22.696219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1800
Q127010
median70000
Q3143000
95-th percentile1200000
Maximum1900000000
Range1900000000
Interquartile range (IQR)115990

Descriptive statistics

Standard deviation55932374.69
Coefficient of variation (CV)17.27285221
Kurtosis803.4420516
Mean3238166.69
Median Absolute Deviation (MAD)54000
Skewness26.99760535
Sum7321494887
Variance3.128430538 × 1015
MonotonicityNot monotonic
2022-08-24T16:41:22.993026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000065
 
2.9%
5000059
 
2.6%
10000045
 
2.0%
12000044
 
1.9%
9000039
 
1.7%
15000039
 
1.7%
20000039
 
1.7%
11000036
 
1.6%
7000035
 
1.5%
5500035
 
1.5%
Other values (545)1825
80.7%
ValueCountFrequency (%)
05
0.2%
12
 
0.1%
61
 
< 0.1%
501
 
< 0.1%
1001
 
< 0.1%
1401
 
< 0.1%
1881
 
< 0.1%
2002
 
0.1%
2501
 
< 0.1%
3002
 
0.1%
ValueCountFrequency (%)
19000000001
< 0.1%
14250000001
< 0.1%
9000000001
< 0.1%
5250000001
< 0.1%
4400000001
< 0.1%
2000000001
< 0.1%
1800000001
< 0.1%
1500000001
< 0.1%
1200000002
0.1%
1000000001
< 0.1%

converted_comp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1178
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119574.7174
Minimum0
Maximum2000000
Zeros9
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-08-24T16:41:23.260111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6360
Q127492
median58373
Q3104208
95-th percentile325000
Maximum2000000
Range2000000
Interquartile range (IQR)76716

Descriptive statistics

Standard deviation265556.0729
Coefficient of variation (CV)2.220837972
Kurtosis32.81638818
Mean119574.7174
Median Absolute Deviation (MAD)35165
Skewness5.481555966
Sum270358436
Variance7.052002786 × 1010
MonotonicityNot monotonic
2022-08-24T16:41:23.625982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6485929
 
1.3%
200000027
 
1.2%
5404923
 
1.0%
11000023
 
1.0%
20000022
 
1.0%
12000019
 
0.8%
7566918
 
0.8%
13000018
 
0.8%
100000018
 
0.8%
7026417
 
0.8%
Other values (1168)2047
90.5%
ValueCountFrequency (%)
09
0.4%
241
 
< 0.1%
251
 
< 0.1%
541
 
< 0.1%
941
 
< 0.1%
1881
 
< 0.1%
6981
 
< 0.1%
8401
 
< 0.1%
8761
 
< 0.1%
9361
 
< 0.1%
ValueCountFrequency (%)
200000027
1.2%
19200002
 
0.1%
18000001
 
< 0.1%
15600001
 
< 0.1%
14400001
 
< 0.1%
13560001
 
< 0.1%
13440001
 
< 0.1%
12600001
 
< 0.1%
12000001
 
< 0.1%
10800001
 
< 0.1%

country
Categorical

HIGH CARDINALITY

Distinct105
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
United States
544 
India
186 
Germany
167 
United Kingdom
157 
France
 
81
Other values (100)
1126 

Length

Max length36
Median length22
Mean length9.232198142
Min length4

Characters and Unicode

Total characters20874
Distinct characters54
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)1.0%

Sample

1st rowUnited Kingdom
2nd rowUnited Kingdom
3rd rowFrance
4th rowUnited States
5th rowCanada

Common Values

ValueCountFrequency (%)
United States544
24.1%
India186
 
8.2%
Germany167
 
7.4%
United Kingdom157
 
6.9%
France81
 
3.6%
Brazil80
 
3.5%
Canada75
 
3.3%
Spain65
 
2.9%
Australia55
 
2.4%
Netherlands54
 
2.4%
Other values (95)797
35.2%

Length

2022-08-24T16:41:23.824387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united707
22.6%
states544
17.4%
india186
 
6.0%
germany167
 
5.3%
kingdom157
 
5.0%
france81
 
2.6%
brazil80
 
2.6%
canada75
 
2.4%
spain65
 
2.1%
australia55
 
1.8%
Other values (113)1007
32.2%

Most occurring characters

ValueCountFrequency (%)
a2347
11.2%
e2213
 
10.6%
t2157
 
10.3%
n2005
 
9.6%
i1763
 
8.4%
d1427
 
6.8%
s875
 
4.2%
863
 
4.1%
r841
 
4.0%
S745
 
3.6%
Other values (44)5638
27.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16846
80.7%
Uppercase Letter3130
 
15.0%
Space Separator863
 
4.1%
Other Punctuation23
 
0.1%
Open Punctuation6
 
< 0.1%
Close Punctuation6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2347
13.9%
e2213
13.1%
t2157
12.8%
n2005
11.9%
i1763
10.5%
d1427
8.5%
s875
 
5.2%
r841
 
5.0%
l534
 
3.2%
o423
 
2.5%
Other values (15)2261
13.4%
Uppercase Letter
ValueCountFrequency (%)
S745
23.8%
U732
23.4%
I292
 
9.3%
G188
 
6.0%
K175
 
5.6%
F143
 
4.6%
C140
 
4.5%
B126
 
4.0%
A118
 
3.8%
N110
 
3.5%
Other values (13)361
11.5%
Other Punctuation
ValueCountFrequency (%)
.21
91.3%
,1
 
4.3%
'1
 
4.3%
Space Separator
ValueCountFrequency (%)
863
100.0%
Open Punctuation
ValueCountFrequency (%)
(6
100.0%
Close Punctuation
ValueCountFrequency (%)
)6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19976
95.7%
Common898
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2347
11.7%
e2213
11.1%
t2157
10.8%
n2005
10.0%
i1763
 
8.8%
d1427
 
7.1%
s875
 
4.4%
r841
 
4.2%
S745
 
3.7%
U732
 
3.7%
Other values (38)4871
24.4%
Common
ValueCountFrequency (%)
863
96.1%
.21
 
2.3%
(6
 
0.7%
)6
 
0.7%
,1
 
0.1%
'1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20873
> 99.9%
None1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2347
11.2%
e2213
 
10.6%
t2157
 
10.3%
n2005
 
9.6%
i1763
 
8.4%
d1427
 
6.8%
s875
 
4.2%
863
 
4.1%
r841
 
4.0%
S745
 
3.6%
Other values (43)5637
27.0%
None
ValueCountFrequency (%)
ô1
100.0%

currency_desc
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
United States dollar
597 
European Euro
557 
Indian rupee
182 
Pound sterling
158 
Brazilian real
79 
Other values (68)
688 

Length

Max length39
Median length27
Mean length15.29500221
Min length9

Characters and Unicode

Total characters34582
Distinct characters50
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.6%

Sample

1st rowPound sterling
2nd rowPound sterling
3rd rowEuropean Euro
4th rowUnited States dollar
5th rowCanadian dollar

Common Values

ValueCountFrequency (%)
United States dollar597
26.4%
European Euro557
24.6%
Indian rupee182
 
8.0%
Pound sterling158
 
7.0%
Brazilian real79
 
3.5%
Canadian dollar75
 
3.3%
Australian dollar55
 
2.4%
Russian ruble43
 
1.9%
Swiss franc40
 
1.8%
Swedish krona37
 
1.6%
Other values (63)438
19.4%

Length

2022-08-24T16:41:23.980636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dollar754
14.5%
united602
11.5%
states597
11.4%
european557
10.7%
euro557
10.7%
rupee219
 
4.2%
indian182
 
3.5%
pound160
 
3.1%
sterling158
 
3.0%
brazilian79
 
1.5%
Other values (122)1352
25.9%

Most occurring characters

ValueCountFrequency (%)
a3437
 
9.9%
e3001
 
8.7%
r2965
 
8.6%
2956
 
8.5%
n2835
 
8.2%
o2384
 
6.9%
l2194
 
6.3%
t2152
 
6.2%
i1886
 
5.5%
d1880
 
5.4%
Other values (40)8892
25.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28131
81.3%
Uppercase Letter3495
 
10.1%
Space Separator2956
 
8.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a3437
12.2%
e3001
10.7%
r2965
10.5%
n2835
10.1%
o2384
8.5%
l2194
7.8%
t2152
7.6%
i1886
6.7%
d1880
6.7%
u1773
6.3%
Other values (15)3624
12.9%
Uppercase Letter
ValueCountFrequency (%)
E1120
32.0%
S710
20.3%
U620
17.7%
I228
 
6.5%
P227
 
6.5%
C132
 
3.8%
A95
 
2.7%
B94
 
2.7%
R57
 
1.6%
N51
 
1.5%
Other values (14)161
 
4.6%
Space Separator
ValueCountFrequency (%)
2956
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin31626
91.5%
Common2956
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a3437
10.9%
e3001
9.5%
r2965
9.4%
n2835
 
9.0%
o2384
 
7.5%
l2194
 
6.9%
t2152
 
6.8%
i1886
 
6.0%
d1880
 
5.9%
u1773
 
5.6%
Other values (39)7119
22.5%
Common
ValueCountFrequency (%)
2956
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII34582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a3437
 
9.9%
e3001
 
8.7%
r2965
 
8.6%
2956
 
8.5%
n2835
 
8.2%
o2384
 
6.9%
l2194
 
6.3%
t2152
 
6.2%
i1886
 
5.5%
d1880
 
5.4%
Other values (40)8892
25.7%

currency_symbol
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
USD
597 
EUR
557 
INR
182 
GBP
158 
BRL
79 
Other values (68)
688 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6783
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.6%

Sample

1st rowGBP
2nd rowGBP
3rd rowEUR
4th rowUSD
5th rowCAD

Common Values

ValueCountFrequency (%)
USD597
26.4%
EUR557
24.6%
INR182
 
8.0%
GBP158
 
7.0%
BRL79
 
3.5%
CAD75
 
3.3%
AUD55
 
2.4%
RUB43
 
1.9%
CHF40
 
1.8%
SEK37
 
1.6%
Other values (63)438
19.4%

Length

2022-08-24T16:41:24.136848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usd597
26.4%
eur557
24.6%
inr182
 
8.0%
gbp158
 
7.0%
brl79
 
3.5%
cad75
 
3.3%
aud55
 
2.4%
rub43
 
1.9%
chf40
 
1.8%
sek37
 
1.6%
Other values (63)438
19.4%

Most occurring characters

ValueCountFrequency (%)
U1284
18.9%
R1011
14.9%
D802
11.8%
S684
10.1%
E608
9.0%
N322
 
4.7%
B299
 
4.4%
P271
 
4.0%
I228
 
3.4%
G190
 
2.8%
Other values (16)1084
16.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6783
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U1284
18.9%
R1011
14.9%
D802
11.8%
S684
10.1%
E608
9.0%
N322
 
4.7%
B299
 
4.4%
P271
 
4.0%
I228
 
3.4%
G190
 
2.8%
Other values (16)1084
16.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6783
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U1284
18.9%
R1011
14.9%
D802
11.8%
S684
10.1%
E608
9.0%
N322
 
4.7%
B299
 
4.4%
P271
 
4.0%
I228
 
3.4%
G190
 
2.8%
Other values (16)1084
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U1284
18.9%
R1011
14.9%
D802
11.8%
S684
10.1%
E608
9.0%
N322
 
4.7%
B299
 
4.4%
P271
 
4.0%
I228
 
3.4%
G190
 
2.8%
Other values (16)1084
16.0%

database_desire_next_year
Categorical

HIGH CARDINALITY
MISSING

Distinct576
Distinct (%)34.1%
Missing574
Missing (%)25.4%
Memory size17.8 KiB
PostgreSQL
 
124
PostgreSQL;SQLite
 
57
MongoDB
 
54
MySQL
 
49
Microsoft SQL Server
 
49
Other values (571)
1354 

Length

Max length133
Median length85
Mean length28.02489627
Min length5

Characters and Unicode

Total characters47278
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique376 ?
Unique (%)22.3%

Sample

1st rowMicrosoft SQL Server;MongoDB;SQLite
2nd rowPostgreSQL;SQLite
3rd rowPostgreSQL
4th rowPostgreSQL
5th rowRedis

Common Values

ValueCountFrequency (%)
PostgreSQL124
 
5.5%
PostgreSQL;SQLite57
 
2.5%
MongoDB54
 
2.4%
MySQL49
 
2.2%
Microsoft SQL Server49
 
2.2%
Elasticsearch;PostgreSQL34
 
1.5%
PostgreSQL;Redis34
 
1.5%
SQLite34
 
1.5%
MongoDB;PostgreSQL25
 
1.1%
Elasticsearch23
 
1.0%
Other values (566)1204
53.3%
(Missing)574
25.4%

Length

2022-08-24T16:41:24.310491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sql342
 
14.2%
microsoft175
 
7.2%
postgresql124
 
5.1%
server62
 
2.6%
postgresql;sqlite57
 
2.4%
mongodb54
 
2.2%
mysql49
 
2.0%
elasticsearch;microsoft37
 
1.5%
elasticsearch;postgresql34
 
1.4%
postgresql;redis34
 
1.4%
Other values (491)1447
59.9%

Most occurring characters

ValueCountFrequency (%)
e3722
 
7.9%
s3525
 
7.5%
;3359
 
7.1%
r3254
 
6.9%
o3133
 
6.6%
a2826
 
6.0%
S2585
 
5.5%
i2254
 
4.8%
L2243
 
4.7%
Q2243
 
4.7%
Other values (25)18134
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29238
61.8%
Uppercase Letter13909
29.4%
Other Punctuation3359
 
7.1%
Space Separator728
 
1.5%
Decimal Number44
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3722
12.7%
s3525
12.1%
r3254
11.1%
o3133
10.7%
a2826
9.7%
i2254
7.7%
t2241
7.7%
c1662
5.7%
g1529
 
5.2%
n1085
 
3.7%
Other values (9)4007
13.7%
Uppercase Letter
ValueCountFrequency (%)
S2585
18.6%
L2243
16.1%
Q2243
16.1%
M1776
12.8%
D1293
9.3%
B1138
8.2%
P878
 
6.3%
E537
 
3.9%
R489
 
3.5%
C305
 
2.2%
Other values (3)422
 
3.0%
Other Punctuation
ValueCountFrequency (%)
;3359
100.0%
Space Separator
ValueCountFrequency (%)
728
100.0%
Decimal Number
ValueCountFrequency (%)
244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin43147
91.3%
Common4131
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3722
 
8.6%
s3525
 
8.2%
r3254
 
7.5%
o3133
 
7.3%
a2826
 
6.5%
S2585
 
6.0%
i2254
 
5.2%
L2243
 
5.2%
Q2243
 
5.2%
t2241
 
5.2%
Other values (22)15121
35.0%
Common
ValueCountFrequency (%)
;3359
81.3%
728
 
17.6%
244
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII47278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3722
 
7.9%
s3525
 
7.5%
;3359
 
7.1%
r3254
 
6.9%
o3133
 
6.6%
a2826
 
6.0%
S2585
 
5.5%
i2254
 
4.8%
L2243
 
4.7%
Q2243
 
4.7%
Other values (25)18134
38.4%

database_worked_with
Categorical

HIGH CARDINALITY
MISSING

Distinct594
Distinct (%)30.7%
Missing327
Missing (%)14.5%
Memory size17.8 KiB
PostgreSQL
 
116
Microsoft SQL Server
 
105
MySQL
 
86
SQLite
 
43
MySQL;PostgreSQL
 
42
Other values (589)
1542 

Length

Max length133
Median length92
Mean length29.38883144
Min length5

Characters and Unicode

Total characters56838
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique374 ?
Unique (%)19.3%

Sample

1st rowIBM DB2;Microsoft SQL Server;MongoDB;SQLite
2nd rowMicrosoft SQL Server;Oracle;PostgreSQL;SQLite
3rd rowMongoDB
4th rowPostgreSQL
5th rowMariaDB

Common Values

ValueCountFrequency (%)
PostgreSQL116
 
5.1%
Microsoft SQL Server105
 
4.6%
MySQL86
 
3.8%
SQLite43
 
1.9%
MySQL;PostgreSQL42
 
1.9%
PostgreSQL;SQLite40
 
1.8%
MySQL;SQLite36
 
1.6%
MySQL;PostgreSQL;SQLite35
 
1.5%
MongoDB34
 
1.5%
Oracle28
 
1.2%
Other values (584)1369
60.5%
(Missing)327
 
14.5%

Length

2022-08-24T16:41:24.544803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sql656
 
19.8%
microsoft373
 
11.2%
server117
 
3.5%
postgresql116
 
3.5%
mysql86
 
2.6%
elasticsearch;microsoft58
 
1.7%
ibm50
 
1.5%
mariadb;microsoft49
 
1.5%
sqlite43
 
1.3%
mysql;postgresql42
 
1.3%
Other values (445)1727
52.1%

Most occurring characters

ValueCountFrequency (%)
e4614
 
8.1%
r4313
 
7.6%
S4086
 
7.2%
;4075
 
7.2%
o3700
 
6.5%
Q3430
 
6.0%
L3430
 
6.0%
s3274
 
5.8%
t2831
 
5.0%
i2686
 
4.7%
Other values (25)20399
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32735
57.6%
Uppercase Letter18574
32.7%
Other Punctuation4075
 
7.2%
Space Separator1383
 
2.4%
Decimal Number71
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4614
14.1%
r4313
13.2%
o3700
11.3%
s3274
10.0%
t2831
8.6%
i2686
8.2%
a2444
7.5%
c1859
5.7%
g1601
 
4.9%
y1157
 
3.5%
Other values (9)4256
13.0%
Uppercase Letter
ValueCountFrequency (%)
S4086
22.0%
Q3430
18.5%
L3430
18.5%
M2630
14.2%
D1273
 
6.9%
B1192
 
6.4%
P1004
 
5.4%
E406
 
2.2%
R380
 
2.0%
O353
 
1.9%
Other values (3)390
 
2.1%
Other Punctuation
ValueCountFrequency (%)
;4075
100.0%
Space Separator
ValueCountFrequency (%)
1383
100.0%
Decimal Number
ValueCountFrequency (%)
271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin51309
90.3%
Common5529
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4614
 
9.0%
r4313
 
8.4%
S4086
 
8.0%
o3700
 
7.2%
Q3430
 
6.7%
L3430
 
6.7%
s3274
 
6.4%
t2831
 
5.5%
i2686
 
5.2%
M2630
 
5.1%
Other values (22)16315
31.8%
Common
ValueCountFrequency (%)
;4075
73.7%
1383
 
25.0%
271
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII56838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4614
 
8.1%
r4313
 
7.6%
S4086
 
7.2%
;4075
 
7.2%
o3700
 
6.5%
Q3430
 
6.0%
L3430
 
6.0%
s3274
 
5.8%
t2831
 
5.0%
i2686
 
4.7%
Other values (25)20399
35.9%

dev_type
Categorical

HIGH CARDINALITY

Distinct995
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
Data scientist or machine learning specialist
198 
Data or business analyst;Data scientist or machine learning specialist
 
115
Data scientist or machine learning specialist;Developer, back-end
 
88
Academic researcher;Data scientist or machine learning specialist
 
59
Academic researcher;Data scientist or machine learning specialist;Scientist
 
53
Other values (990)
1748 

Length

Max length531
Median length364
Mean length118.5696594
Min length45

Characters and Unicode

Total characters268086
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique806 ?
Unique (%)35.6%

Sample

1st rowData or business analyst;Data scientist or machine learning specialist
2nd rowData scientist or machine learning specialist;Developer, back-end;Developer, QA or test;Engineer, data;Scientist
3rd rowData scientist or machine learning specialist;Database administrator;Developer, back-end;Developer, full-stack;Engineer, data
4th rowData scientist or machine learning specialist;Scientist
5th rowData scientist or machine learning specialist;Database administrator;Designer;Developer, back-end;Developer, front-end;Developer, full-stack

Common Values

ValueCountFrequency (%)
Data scientist or machine learning specialist198
 
8.8%
Data or business analyst;Data scientist or machine learning specialist115
 
5.1%
Data scientist or machine learning specialist;Developer, back-end88
 
3.9%
Academic researcher;Data scientist or machine learning specialist59
 
2.6%
Academic researcher;Data scientist or machine learning specialist;Scientist53
 
2.3%
Data scientist or machine learning specialist;Developer, full-stack48
 
2.1%
Data scientist or machine learning specialist;Engineer, data46
 
2.0%
Data scientist or machine learning specialist;Developer, back-end;Engineer, data40
 
1.8%
Data or business analyst;Data scientist or machine learning specialist;Engineer, data37
 
1.6%
Data scientist or machine learning specialist;Scientist36
 
1.6%
Other values (985)1541
68.2%

Length

2022-08-24T16:41:24.748152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
or3875
15.9%
scientist2261
 
9.3%
learning2261
 
9.3%
machine2261
 
9.3%
data2028
 
8.3%
specialist;developer1051
 
4.3%
back-end;developer709
 
2.9%
business680
 
2.8%
analyst;data680
 
2.8%
academic585
 
2.4%
Other values (179)8022
32.9%

Most occurring characters

ValueCountFrequency (%)
e32112
12.0%
a23745
 
8.9%
22152
 
8.3%
i22006
 
8.2%
s18221
 
6.8%
t18039
 
6.7%
n17162
 
6.4%
r15992
 
6.0%
c12715
 
4.7%
l11701
 
4.4%
Other values (26)74241
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter220731
82.3%
Space Separator22152
 
8.3%
Other Punctuation12110
 
4.5%
Uppercase Letter10820
 
4.0%
Dash Punctuation2273
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e32112
14.5%
a23745
10.8%
i22006
10.0%
s18221
8.3%
t18039
8.2%
n17162
7.8%
r15992
7.2%
c12715
 
5.8%
l11701
 
5.3%
o10172
 
4.6%
Other values (12)38866
17.6%
Uppercase Letter
ValueCountFrequency (%)
D7223
66.8%
E1214
 
11.2%
S859
 
7.9%
A731
 
6.8%
O314
 
2.9%
P227
 
2.1%
Q146
 
1.3%
V79
 
0.7%
M27
 
0.2%
Other Punctuation
ValueCountFrequency (%)
;7795
64.4%
,4236
35.0%
/79
 
0.7%
Space Separator
ValueCountFrequency (%)
22152
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2273
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin231551
86.4%
Common36535
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e32112
13.9%
a23745
10.3%
i22006
9.5%
s18221
 
7.9%
t18039
 
7.8%
n17162
 
7.4%
r15992
 
6.9%
c12715
 
5.5%
l11701
 
5.1%
o10172
 
4.4%
Other values (21)49686
21.5%
Common
ValueCountFrequency (%)
22152
60.6%
;7795
 
21.3%
,4236
 
11.6%
-2273
 
6.2%
/79
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII268086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e32112
12.0%
a23745
 
8.9%
22152
 
8.3%
i22006
 
8.2%
s18221
 
6.8%
t18039
 
6.7%
n17162
 
6.4%
r15992
 
6.0%
c12715
 
4.7%
l11701
 
4.4%
Other values (26)74241
27.7%

ed_level
Categorical

Distinct9
Distinct (%)0.4%
Missing15
Missing (%)0.7%
Memory size17.8 KiB
Master’s degree (M.A., M.S., M.Eng., MBA, etc.)
961 
Bachelor’s degree (B.A., B.S., B.Eng., etc.)
743 
Other doctoral degree (Ph.D., Ed.D., etc.)
346 
Some college/university study without earning a degree
125 
Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)
 
27
Other values (4)
 
44

Length

Max length82
Median length54
Mean length45.78895815
Min length25

Characters and Unicode

Total characters102842
Distinct characters39
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSome college/university study without earning a degree
2nd rowOther doctoral degree (Ph.D., Ed.D., etc.)
3rd rowMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)
4th rowOther doctoral degree (Ph.D., Ed.D., etc.)
5th rowBachelor’s degree (B.A., B.S., B.Eng., etc.)

Common Values

ValueCountFrequency (%)
Master’s degree (M.A., M.S., M.Eng., MBA, etc.)961
42.5%
Bachelor’s degree (B.A., B.S., B.Eng., etc.)743
32.9%
Other doctoral degree (Ph.D., Ed.D., etc.)346
 
15.3%
Some college/university study without earning a degree125
 
5.5%
Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)27
 
1.2%
Professional degree (JD, MD, etc.)18
 
0.8%
Associate degree (A.A., A.S., etc.)16
 
0.7%
I never completed any formal education5
 
0.2%
Primary/elementary school5
 
0.2%
(Missing)15
 
0.7%

Length

2022-08-24T16:41:24.942225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:25.146415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
degree2209
15.1%
etc2111
14.4%
master’s961
 
6.6%
m.a961
 
6.6%
m.s961
 
6.6%
m.eng961
 
6.6%
mba961
 
6.6%
bachelor’s743
 
5.1%
b.a743
 
5.1%
b.s743
 
5.1%
Other values (33)3289
22.5%

Most occurring characters

ValueCountFrequency (%)
.13837
13.5%
12397
 
12.1%
e11649
 
11.3%
,6887
 
6.7%
r5006
 
4.9%
M4823
 
4.7%
t4295
 
4.2%
g4217
 
4.1%
B3933
 
3.8%
c3491
 
3.4%
Other values (29)32307
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter46684
45.4%
Other Punctuation20854
20.3%
Uppercase Letter16981
 
16.5%
Space Separator12397
 
12.1%
Close Punctuation2111
 
2.1%
Open Punctuation2111
 
2.1%
Final Punctuation1704
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e11649
25.0%
r5006
10.7%
t4295
 
9.2%
g4217
 
9.0%
c3491
 
7.5%
s3096
 
6.6%
d3063
 
6.6%
a2494
 
5.3%
n2225
 
4.8%
o2049
 
4.4%
Other values (10)5099
10.9%
Uppercase Letter
ValueCountFrequency (%)
M4823
28.4%
B3933
23.2%
A2756
16.2%
E2050
12.1%
S1872
 
11.0%
D728
 
4.3%
P369
 
2.2%
O346
 
2.0%
G54
 
0.3%
R27
 
0.2%
Other values (2)23
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.13837
66.4%
,6887
33.0%
/130
 
0.6%
Space Separator
ValueCountFrequency (%)
12397
100.0%
Close Punctuation
ValueCountFrequency (%)
)2111
100.0%
Open Punctuation
ValueCountFrequency (%)
(2111
100.0%
Final Punctuation
ValueCountFrequency (%)
1704
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin63665
61.9%
Common39177
38.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e11649
18.3%
r5006
 
7.9%
M4823
 
7.6%
t4295
 
6.7%
g4217
 
6.6%
B3933
 
6.2%
c3491
 
5.5%
s3096
 
4.9%
d3063
 
4.8%
A2756
 
4.3%
Other values (22)17336
27.2%
Common
ValueCountFrequency (%)
.13837
35.3%
12397
31.6%
,6887
17.6%
)2111
 
5.4%
(2111
 
5.4%
1704
 
4.3%
/130
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII101138
98.3%
Punctuation1704
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.13837
13.7%
12397
12.3%
e11649
 
11.5%
,6887
 
6.8%
r5006
 
4.9%
M4823
 
4.8%
t4295
 
4.2%
g4217
 
4.2%
B3933
 
3.9%
c3491
 
3.5%
Other values (28)30603
30.3%
Punctuation
ValueCountFrequency (%)
1704
100.0%

employment
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
Employed full-time
1965 
Independent contractor, freelancer, or self-employed
200 
Employed part-time
 
96

Length

Max length52
Median length18
Mean length21.0075188
Min length18

Characters and Unicode

Total characters47498
Distinct characters21
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmployed full-time
2nd rowEmployed full-time
3rd rowEmployed full-time
4th rowEmployed full-time
5th rowEmployed full-time

Common Values

ValueCountFrequency (%)
Employed full-time1965
86.9%
Independent contractor, freelancer, or self-employed200
 
8.8%
Employed part-time96
 
4.2%

Length

2022-08-24T16:41:25.375890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:25.532103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
employed2061
40.2%
full-time1965
38.4%
independent200
 
3.9%
contractor200
 
3.9%
freelancer200
 
3.9%
or200
 
3.9%
self-employed200
 
3.9%
part-time96
 
1.9%

Most occurring characters

ValueCountFrequency (%)
l6591
13.9%
e5922
12.5%
m4322
 
9.1%
o2861
 
6.0%
2861
 
6.0%
t2757
 
5.8%
d2661
 
5.6%
p2557
 
5.4%
f2365
 
5.0%
y2261
 
4.8%
Other values (11)12340
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter39715
83.6%
Space Separator2861
 
6.0%
Dash Punctuation2261
 
4.8%
Uppercase Letter2261
 
4.8%
Other Punctuation400
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l6591
16.6%
e5922
14.9%
m4322
10.9%
o2861
7.2%
t2757
6.9%
d2661
6.7%
p2557
 
6.4%
f2365
 
6.0%
y2261
 
5.7%
i2061
 
5.2%
Other values (6)5357
13.5%
Uppercase Letter
ValueCountFrequency (%)
E2061
91.2%
I200
 
8.8%
Space Separator
ValueCountFrequency (%)
2861
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2261
100.0%
Other Punctuation
ValueCountFrequency (%)
,400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin41976
88.4%
Common5522
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
l6591
15.7%
e5922
14.1%
m4322
10.3%
o2861
 
6.8%
t2757
 
6.6%
d2661
 
6.3%
p2557
 
6.1%
f2365
 
5.6%
y2261
 
5.4%
E2061
 
4.9%
Other values (8)7618
18.1%
Common
ValueCountFrequency (%)
2861
51.8%
-2261
40.9%
,400
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII47498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l6591
13.9%
e5922
12.5%
m4322
 
9.1%
o2861
 
6.0%
2861
 
6.0%
t2757
 
5.8%
d2661
 
5.6%
p2557
 
5.4%
f2365
 
5.0%
y2261
 
4.8%
Other values (11)12340
26.0%

ethnicity
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct51
Distinct (%)2.4%
Missing169
Missing (%)7.5%
Memory size17.8 KiB
White or of European descent
1443 
South Asian
192 
Hispanic or Latino/a/x
 
95
Middle Eastern
 
60
East Asian
 
55
Other values (46)
247 

Length

Max length235
Median length28
Mean length26.86137667
Min length8

Characters and Unicode

Total characters56194
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)1.2%

Sample

1st rowWhite or of European descent
2nd rowWhite or of European descent
3rd rowWhite or of European descent
4th rowWhite or of European descent
5th rowWhite or of European descent

Common Values

ValueCountFrequency (%)
White or of European descent1443
63.8%
South Asian192
 
8.5%
Hispanic or Latino/a/x95
 
4.2%
Middle Eastern60
 
2.7%
East Asian55
 
2.4%
Southeast Asian50
 
2.2%
Hispanic or Latino/a/x;White or of European descent44
 
1.9%
Black or of African descent40
 
1.8%
Middle Eastern;White or of European descent18
 
0.8%
Multiracial11
 
0.5%
Other values (41)84
 
3.7%
(Missing)169
 
7.5%

Length

2022-08-24T16:41:25.709093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
or1803
19.0%
of1624
17.1%
european1561
16.5%
descent1554
16.4%
white1459
15.4%
asian310
 
3.3%
south194
 
2.0%
hispanic156
 
1.6%
latino/a/x98
 
1.0%
middle81
 
0.9%
Other values (46)634
 
6.7%

Most occurring characters

ValueCountFrequency (%)
7382
13.1%
e6686
11.9%
o5443
 
9.7%
n4104
 
7.3%
t3906
 
7.0%
r3630
 
6.5%
a2981
 
5.3%
i2782
 
5.0%
s2437
 
4.3%
c2043
 
3.6%
Other values (27)14800
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter43625
77.6%
Space Separator7382
 
13.1%
Uppercase Letter4596
 
8.2%
Other Punctuation561
 
1.0%
Open Punctuation15
 
< 0.1%
Close Punctuation15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6686
15.3%
o5443
12.5%
n4104
9.4%
t3906
9.0%
r3630
8.3%
a2981
 
6.8%
i2782
 
6.4%
s2437
 
5.6%
c2043
 
4.7%
u1923
 
4.4%
Other values (10)7690
17.6%
Uppercase Letter
ValueCountFrequency (%)
E1723
37.5%
W1561
34.0%
A427
 
9.3%
S261
 
5.7%
H164
 
3.6%
L164
 
3.6%
M130
 
2.8%
B91
 
2.0%
I45
 
1.0%
N15
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/328
58.5%
;203
36.2%
,30
 
5.3%
Space Separator
ValueCountFrequency (%)
7382
100.0%
Open Punctuation
ValueCountFrequency (%)
(15
100.0%
Close Punctuation
ValueCountFrequency (%)
)15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin48221
85.8%
Common7973
 
14.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6686
13.9%
o5443
11.3%
n4104
 
8.5%
t3906
 
8.1%
r3630
 
7.5%
a2981
 
6.2%
i2782
 
5.8%
s2437
 
5.1%
c2043
 
4.2%
u1923
 
4.0%
Other values (21)12286
25.5%
Common
ValueCountFrequency (%)
7382
92.6%
/328
 
4.1%
;203
 
2.5%
,30
 
0.4%
(15
 
0.2%
)15
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII56194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7382
13.1%
e6686
11.9%
o5443
 
9.7%
n4104
 
7.3%
t3906
 
7.0%
r3630
 
6.5%
a2981
 
5.3%
i2782
 
5.0%
s2437
 
4.3%
c2043
 
3.6%
Other values (27)14800
26.3%

gender
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.3%
Missing33
Missing (%)1.5%
Memory size17.8 KiB
Man
1997 
Woman
 
199
Non-binary, genderqueer, or gender non-conforming
 
16
Man;Non-binary, genderqueer, or gender non-conforming
 
10
Woman;Non-binary, genderqueer, or gender non-conforming
 
5

Length

Max length59
Median length3
Mean length3.875224417
Min length3

Characters and Unicode

Total characters8634
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMan
2nd rowMan
3rd rowMan
4th rowMan
5th rowMan

Common Values

ValueCountFrequency (%)
Man1997
88.3%
Woman199
 
8.8%
Non-binary, genderqueer, or gender non-conforming16
 
0.7%
Man;Non-binary, genderqueer, or gender non-conforming10
 
0.4%
Woman;Non-binary, genderqueer, or gender non-conforming5
 
0.2%
Woman;Man;Non-binary, genderqueer, or gender non-conforming1
 
< 0.1%
(Missing)33
 
1.5%

Length

2022-08-24T16:41:25.889579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:26.057330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
man1997
84.8%
woman199
 
8.4%
genderqueer32
 
1.4%
or32
 
1.4%
gender32
 
1.4%
non-conforming32
 
1.4%
non-binary16
 
0.7%
man;non-binary10
 
0.4%
woman;non-binary5
 
0.2%
woman;man;non-binary1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n2469
28.6%
a2245
26.0%
M2008
23.3%
o365
 
4.2%
m237
 
2.7%
W205
 
2.4%
e192
 
2.2%
r192
 
2.2%
128
 
1.5%
g96
 
1.1%
Other values (12)497
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6116
70.8%
Uppercase Letter2245
 
26.0%
Space Separator128
 
1.5%
Other Punctuation81
 
0.9%
Dash Punctuation64
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n2469
40.4%
a2245
36.7%
o365
 
6.0%
m237
 
3.9%
e192
 
3.1%
r192
 
3.1%
g96
 
1.6%
d64
 
1.0%
i64
 
1.0%
u32
 
0.5%
Other values (5)160
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
M2008
89.4%
W205
 
9.1%
N32
 
1.4%
Other Punctuation
ValueCountFrequency (%)
,64
79.0%
;17
 
21.0%
Space Separator
ValueCountFrequency (%)
128
100.0%
Dash Punctuation
ValueCountFrequency (%)
-64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8361
96.8%
Common273
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n2469
29.5%
a2245
26.9%
M2008
24.0%
o365
 
4.4%
m237
 
2.8%
W205
 
2.5%
e192
 
2.3%
r192
 
2.3%
g96
 
1.1%
d64
 
0.8%
Other values (8)288
 
3.4%
Common
ValueCountFrequency (%)
128
46.9%
,64
23.4%
-64
23.4%
;17
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII8634
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n2469
28.6%
a2245
26.0%
M2008
23.3%
o365
 
4.2%
m237
 
2.7%
W205
 
2.4%
e192
 
2.2%
r192
 
2.2%
128
 
1.5%
g96
 
1.1%
Other values (12)497
 
5.8%

job_factors
Categorical

HIGH CARDINALITY
MISSING

Distinct180
Distinct (%)8.1%
Missing28
Missing (%)1.2%
Memory size17.8 KiB
How widely used or impactful my work output would be;Office environment or company culture;Opportunities for professional development
 
86
Languages, frameworks, and other technologies I’d be working with;Office environment or company culture;Opportunities for professional development
 
78
Flex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Remote work options
 
74
Flex time or a flexible schedule;Office environment or company culture;Opportunities for professional development
 
71
Flex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Office environment or company culture
 
68
Other values (175)
1856 

Length

Max length189
Median length155
Mean length124.1988356
Min length19

Characters and Unicode

Total characters277336
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)1.1%

Sample

1st rowFlex time or a flexible schedule;Office environment or company culture;Opportunities for professional development
2nd rowRemote work options;How widely used or impactful my work output would be;Opportunities for professional development
3rd rowFlex time or a flexible schedule;How widely used or impactful my work output would be;Opportunities for professional development
4th rowFlex time or a flexible schedule;Family friendliness
5th rowDiversity of the company or organization;Flex time or a flexible schedule;Office environment or company culture

Common Values

ValueCountFrequency (%)
How widely used or impactful my work output would be;Office environment or company culture;Opportunities for professional development86
 
3.8%
Languages, frameworks, and other technologies I’d be working with;Office environment or company culture;Opportunities for professional development78
 
3.4%
Flex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Remote work options74
 
3.3%
Flex time or a flexible schedule;Office environment or company culture;Opportunities for professional development71
 
3.1%
Flex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Office environment or company culture68
 
3.0%
Flex time or a flexible schedule;Remote work options;Office environment or company culture65
 
2.9%
Flex time or a flexible schedule;Remote work options;Opportunities for professional development63
 
2.8%
Languages, frameworks, and other technologies I’d be working with;How widely used or impactful my work output would be;Office environment or company culture61
 
2.7%
Flex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Opportunities for professional development51
 
2.3%
Languages, frameworks, and other technologies I’d be working with;How widely used or impactful my work output would be;Opportunities for professional development46
 
2.0%
Other values (170)1570
69.4%

Length

2022-08-24T16:41:26.382108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
or3591
 
9.8%
be1908
 
5.2%
working1703
 
4.7%
i’d1703
 
4.7%
work1383
 
3.8%
company1363
 
3.7%
for1007
 
2.8%
professional1007
 
2.8%
environment961
 
2.6%
development926
 
2.5%
Other values (89)20972
57.4%

Most occurring characters

ValueCountFrequency (%)
34291
 
12.4%
e27005
 
9.7%
o23539
 
8.5%
n16432
 
5.9%
r16393
 
5.9%
i16145
 
5.8%
t15883
 
5.7%
a10727
 
3.9%
l10400
 
3.7%
s9579
 
3.5%
Other values (26)96942
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter226842
81.8%
Space Separator34291
 
12.4%
Uppercase Letter8332
 
3.0%
Other Punctuation6168
 
2.2%
Final Punctuation1703
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e27005
 
11.9%
o23539
 
10.4%
n16432
 
7.2%
r16393
 
7.2%
i16145
 
7.1%
t15883
 
7.0%
a10727
 
4.7%
l10400
 
4.6%
s9579
 
4.2%
u9197
 
4.1%
Other values (14)71542
31.5%
Uppercase Letter
ValueCountFrequency (%)
I2151
25.8%
O1968
23.6%
F1408
16.9%
L886
10.6%
H710
 
8.5%
R673
 
8.1%
S369
 
4.4%
D167
 
2.0%
Other Punctuation
ValueCountFrequency (%)
;4396
71.3%
,1772
28.7%
Space Separator
ValueCountFrequency (%)
34291
100.0%
Final Punctuation
ValueCountFrequency (%)
1703
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin235174
84.8%
Common42162
 
15.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e27005
 
11.5%
o23539
 
10.0%
n16432
 
7.0%
r16393
 
7.0%
i16145
 
6.9%
t15883
 
6.8%
a10727
 
4.6%
l10400
 
4.4%
s9579
 
4.1%
u9197
 
3.9%
Other values (22)79874
34.0%
Common
ValueCountFrequency (%)
34291
81.3%
;4396
 
10.4%
,1772
 
4.2%
1703
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII275633
99.4%
Punctuation1703
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34291
 
12.4%
e27005
 
9.8%
o23539
 
8.5%
n16432
 
6.0%
r16393
 
5.9%
i16145
 
5.9%
t15883
 
5.8%
a10727
 
3.9%
l10400
 
3.8%
s9579
 
3.5%
Other values (25)95239
34.6%
Punctuation
ValueCountFrequency (%)
1703
100.0%

job_sat
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Very satisfied
879 
Slightly satisfied
680 
Slightly dissatisfied
342 
Neither
201 
Very dissatisfied
159 

Length

Max length21
Median length18
Mean length15.85050862
Min length7

Characters and Unicode

Total characters35838
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSlightly satisfied
2nd rowVery satisfied
3rd rowVery satisfied
4th rowVery satisfied
5th rowVery satisfied

Common Values

ValueCountFrequency (%)
Very satisfied879
38.9%
Slightly satisfied680
30.1%
Slightly dissatisfied342
 
15.1%
Neither201
 
8.9%
Very dissatisfied159
 
7.0%

Length

2022-08-24T16:41:26.553979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:26.697459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
satisfied1559
36.1%
very1038
24.0%
slightly1022
23.7%
dissatisfied501
 
11.6%
neither201
 
4.7%

Most occurring characters

ValueCountFrequency (%)
i5844
16.3%
s4621
12.9%
e3500
9.8%
t3283
9.2%
d2561
7.1%
y2060
 
5.7%
2060
 
5.7%
a2060
 
5.7%
f2060
 
5.7%
l2044
 
5.7%
Other values (6)5745
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31517
87.9%
Uppercase Letter2261
 
6.3%
Space Separator2060
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i5844
18.5%
s4621
14.7%
e3500
11.1%
t3283
10.4%
d2561
8.1%
y2060
 
6.5%
a2060
 
6.5%
f2060
 
6.5%
l2044
 
6.5%
r1239
 
3.9%
Other values (2)2245
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
V1038
45.9%
S1022
45.2%
N201
 
8.9%
Space Separator
ValueCountFrequency (%)
2060
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin33778
94.3%
Common2060
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i5844
17.3%
s4621
13.7%
e3500
10.4%
t3283
9.7%
d2561
7.6%
y2060
 
6.1%
a2060
 
6.1%
f2060
 
6.1%
l2044
 
6.1%
r1239
 
3.7%
Other values (5)4506
13.3%
Common
ValueCountFrequency (%)
2060
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i5844
16.3%
s4621
12.9%
e3500
9.8%
t3283
9.2%
d2561
7.1%
y2060
 
5.7%
2060
 
5.7%
a2060
 
5.7%
f2060
 
5.7%
l2044
 
5.7%
Other values (6)5745
16.0%

job_seek
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
I’m not actively looking, but I am open to new opportunities
1330 
I am not interested in new job opportunities
590 
I am actively looking for a job
341 

Length

Max length60
Median length60
Mean length51.45112782
Min length31

Characters and Unicode

Total characters116331
Distinct characters26
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI’m not actively looking, but I am open to new opportunities
2nd rowI’m not actively looking, but I am open to new opportunities
3rd rowI am not interested in new job opportunities
4th rowI am not interested in new job opportunities
5th rowI am not interested in new job opportunities

Common Values

ValueCountFrequency (%)
I’m not actively looking, but I am open to new opportunities1330
58.8%
I am not interested in new job opportunities590
26.1%
I am actively looking for a job341
 
15.1%

Length

2022-08-24T16:41:26.864326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:27.004959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
i2261
10.4%
am2261
10.4%
not1920
8.8%
new1920
8.8%
opportunities1920
8.8%
actively1671
7.7%
looking1671
7.7%
i’m1330
 
6.1%
but1330
 
6.1%
open1330
 
6.1%
Other values (6)4123
19.0%

Most occurring characters

ValueCountFrequency (%)
19476
16.7%
o13034
11.2%
t11271
 
9.7%
n9941
 
8.5%
e8611
 
7.4%
i8362
 
7.2%
p5170
 
4.4%
a4273
 
3.7%
I3591
 
3.1%
m3591
 
3.1%
Other values (16)29011
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter90604
77.9%
Space Separator19476
 
16.7%
Uppercase Letter3591
 
3.1%
Other Punctuation1330
 
1.1%
Final Punctuation1330
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o13034
14.4%
t11271
12.4%
n9941
11.0%
e8611
9.5%
i8362
9.2%
p5170
 
5.7%
a4273
 
4.7%
m3591
 
4.0%
l3342
 
3.7%
u3250
 
3.6%
Other values (12)19759
21.8%
Space Separator
ValueCountFrequency (%)
19476
100.0%
Uppercase Letter
ValueCountFrequency (%)
I3591
100.0%
Other Punctuation
ValueCountFrequency (%)
,1330
100.0%
Final Punctuation
ValueCountFrequency (%)
1330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin94195
81.0%
Common22136
 
19.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o13034
13.8%
t11271
12.0%
n9941
10.6%
e8611
 
9.1%
i8362
 
8.9%
p5170
 
5.5%
a4273
 
4.5%
I3591
 
3.8%
m3591
 
3.8%
l3342
 
3.5%
Other values (13)23009
24.4%
Common
ValueCountFrequency (%)
19476
88.0%
,1330
 
6.0%
1330
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII115001
98.9%
Punctuation1330
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19476
16.9%
o13034
11.3%
t11271
9.8%
n9941
 
8.6%
e8611
 
7.5%
i8362
 
7.3%
p5170
 
4.5%
a4273
 
3.7%
I3591
 
3.1%
m3591
 
3.1%
Other values (15)27681
24.1%
Punctuation
ValueCountFrequency (%)
1330
100.0%

language_desire_next_year
Categorical

HIGH CARDINALITY
MISSING

Distinct1335
Distinct (%)61.8%
Missing101
Missing (%)4.5%
Memory size17.8 KiB
Python
 
92
Python;R;SQL
 
41
Python;R
 
33
Python;SQL
 
31
Bash/Shell/PowerShell;Python;SQL
 
21
Other values (1330)
1942 

Length

Max length164
Median length82
Mean length30.74675926
Min length1

Characters and Unicode

Total characters66413
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1088 ?
Unique (%)50.4%

Sample

1st rowC#;Go;HTML/CSS;JavaScript;Python;SQL
2nd rowBash/Shell/PowerShell;Java;Python;SQL
3rd rowPython;Rust;Scala;SQL
4th rowPython
5th rowRust

Common Values

ValueCountFrequency (%)
Python92
 
4.1%
Python;R;SQL41
 
1.8%
Python;R33
 
1.5%
Python;SQL31
 
1.4%
Bash/Shell/PowerShell;Python;SQL21
 
0.9%
C++;Python21
 
0.9%
Rust21
 
0.9%
Julia19
 
0.8%
Bash/Shell/PowerShell;Python17
 
0.8%
Python;Rust15
 
0.7%
Other values (1325)1849
81.8%
(Missing)101
 
4.5%

Length

2022-08-24T16:41:27.198599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
python92
 
4.3%
python;r;sql41
 
1.9%
python;r33
 
1.5%
python;sql31
 
1.4%
bash/shell/powershell;python;sql21
 
1.0%
c++;python21
 
1.0%
rust21
 
1.0%
julia19
 
0.9%
bash/shell/powershell;python17
 
0.8%
python;rust15
 
0.7%
Other values (1323)1849
85.6%

Most occurring characters

ValueCountFrequency (%)
;7531
 
11.3%
S4970
 
7.5%
a4067
 
6.1%
l3980
 
6.0%
t3779
 
5.7%
h3660
 
5.5%
o3072
 
4.6%
e2776
 
4.2%
P2629
 
4.0%
y2231
 
3.4%
Other values (33)27718
41.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter37219
56.0%
Uppercase Letter18388
27.7%
Other Punctuation9770
 
14.7%
Math Symbol988
 
1.5%
Dash Punctuation48
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a4067
10.9%
l3980
10.7%
t3779
10.2%
h3660
9.8%
o3072
 
8.3%
e2776
 
7.5%
y2231
 
6.0%
r1940
 
5.2%
n1887
 
5.1%
i1863
 
5.0%
Other values (11)7964
21.4%
Uppercase Letter
ValueCountFrequency (%)
S4970
27.0%
P2629
14.3%
C1716
 
9.3%
L1488
 
8.1%
J1402
 
7.6%
R1104
 
6.0%
Q934
 
5.1%
T934
 
5.1%
H842
 
4.6%
B717
 
3.9%
Other values (7)1652
 
9.0%
Other Punctuation
ValueCountFrequency (%)
;7531
77.1%
/1882
 
19.3%
#357
 
3.7%
Math Symbol
ValueCountFrequency (%)
+988
100.0%
Dash Punctuation
ValueCountFrequency (%)
-48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin55607
83.7%
Common10806
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S4970
 
8.9%
a4067
 
7.3%
l3980
 
7.2%
t3779
 
6.8%
h3660
 
6.6%
o3072
 
5.5%
e2776
 
5.0%
P2629
 
4.7%
y2231
 
4.0%
r1940
 
3.5%
Other values (28)22503
40.5%
Common
ValueCountFrequency (%)
;7531
69.7%
/1882
 
17.4%
+988
 
9.1%
#357
 
3.3%
-48
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII66413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
;7531
 
11.3%
S4970
 
7.5%
a4067
 
6.1%
l3980
 
6.0%
t3779
 
5.7%
h3660
 
5.5%
o3072
 
4.6%
e2776
 
4.2%
P2629
 
4.0%
y2231
 
3.4%
Other values (33)27718
41.7%

language_worked_with
Categorical

HIGH CARDINALITY

Distinct1271
Distinct (%)56.4%
Missing7
Missing (%)0.3%
Memory size17.8 KiB
Python
 
81
Python;R;SQL
 
63
Python;SQL
 
46
Python;R
 
35
Bash/Shell/PowerShell;Python;SQL
 
31
Other values (1266)
1998 

Length

Max length143
Median length89
Mean length38.108252
Min length1

Characters and Unicode

Total characters85896
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1025 ?
Unique (%)45.5%

Sample

1st rowC#;Go;HTML/CSS;Java;JavaScript;Python;R;SQL
2nd rowBash/Shell/PowerShell;C#;Java;JavaScript;Python;Ruby;SQL
3rd rowHTML/CSS;Python
4th rowPython;SQL
5th rowHTML/CSS;JavaScript;Python;TypeScript

Common Values

ValueCountFrequency (%)
Python81
 
3.6%
Python;R;SQL63
 
2.8%
Python;SQL46
 
2.0%
Python;R35
 
1.5%
Bash/Shell/PowerShell;Python;SQL31
 
1.4%
Bash/Shell/PowerShell;Python27
 
1.2%
Bash/Shell/PowerShell;Python;R;SQL26
 
1.1%
Bash/Shell/PowerShell;HTML/CSS;JavaScript;Python;SQL22
 
1.0%
R18
 
0.8%
HTML/CSS;JavaScript;Python18
 
0.8%
Other values (1261)1887
83.5%

Length

2022-08-24T16:41:27.417331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
python81
 
3.6%
python;r;sql63
 
2.8%
python;sql46
 
2.0%
python;r35
 
1.6%
bash/shell/powershell;python;sql31
 
1.4%
bash/shell/powershell;python27
 
1.2%
bash/shell/powershell;python;r;sql26
 
1.2%
bash/shell/powershell;html/css;javascript;python;sql22
 
1.0%
r18
 
0.8%
html/css;javascript;python18
 
0.8%
Other values (1261)1887
83.7%

Most occurring characters

ValueCountFrequency (%)
;9645
 
11.2%
S7401
 
8.6%
a5406
 
6.3%
h5037
 
5.9%
l4907
 
5.7%
e3848
 
4.5%
t3812
 
4.4%
P3750
 
4.4%
o3298
 
3.8%
/3164
 
3.7%
Other values (33)35628
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter44262
51.5%
Uppercase Letter26990
31.4%
Other Punctuation13271
 
15.5%
Math Symbol1302
 
1.5%
Dash Punctuation71
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a5406
12.2%
h5037
11.4%
l4907
11.1%
e3848
8.7%
t3812
8.6%
o3298
 
7.5%
r2618
 
5.9%
y2492
 
5.6%
n2039
 
4.6%
v1939
 
4.4%
Other values (11)8866
20.0%
Uppercase Letter
ValueCountFrequency (%)
S7401
27.4%
P3750
13.9%
C2775
 
10.3%
L2505
 
9.3%
J1963
 
7.3%
H1509
 
5.6%
Q1415
 
5.2%
T1413
 
5.2%
B1239
 
4.6%
M1090
 
4.0%
Other values (7)1930
 
7.2%
Other Punctuation
ValueCountFrequency (%)
;9645
72.7%
/3164
 
23.8%
#462
 
3.5%
Math Symbol
ValueCountFrequency (%)
+1302
100.0%
Dash Punctuation
ValueCountFrequency (%)
-71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin71252
83.0%
Common14644
 
17.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S7401
 
10.4%
a5406
 
7.6%
h5037
 
7.1%
l4907
 
6.9%
e3848
 
5.4%
t3812
 
5.4%
P3750
 
5.3%
o3298
 
4.6%
C2775
 
3.9%
r2618
 
3.7%
Other values (28)28400
39.9%
Common
ValueCountFrequency (%)
;9645
65.9%
/3164
 
21.6%
+1302
 
8.9%
#462
 
3.2%
-71
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII85896
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
;9645
 
11.2%
S7401
 
8.6%
a5406
 
6.3%
h5037
 
5.9%
l4907
 
5.7%
e3848
 
4.5%
t3812
 
4.4%
P3750
 
4.4%
o3298
 
3.8%
/3164
 
3.7%
Other values (33)35628
41.5%

misc_tech_desire_next_year
Categorical

HIGH CARDINALITY
MISSING

Distinct783
Distinct (%)40.1%
Missing307
Missing (%)13.6%
Memory size17.8 KiB
Pandas
 
70
Keras;Pandas;TensorFlow;Torch/PyTorch
 
66
Keras;Pandas;TensorFlow
 
50
Pandas;TensorFlow
 
48
Apache Spark;Hadoop;Keras;Pandas;TensorFlow;Torch/PyTorch
 
46
Other values (778)
1674 

Length

Max length169
Median length95
Mean length35.44370522
Min length4

Characters and Unicode

Total characters69257
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique559 ?
Unique (%)28.6%

Sample

1st rowKeras;Node.js;Pandas;TensorFlow
2nd rowPandas
3rd rowKeras;Pandas;TensorFlow
4th rowKeras;Pandas;TensorFlow;Torch/PyTorch
5th row.NET Core

Common Values

ValueCountFrequency (%)
Pandas70
 
3.1%
Keras;Pandas;TensorFlow;Torch/PyTorch66
 
2.9%
Keras;Pandas;TensorFlow50
 
2.2%
Pandas;TensorFlow48
 
2.1%
Apache Spark;Hadoop;Keras;Pandas;TensorFlow;Torch/PyTorch46
 
2.0%
Pandas;Torch/PyTorch44
 
1.9%
Torch/PyTorch42
 
1.9%
Pandas;TensorFlow;Torch/PyTorch40
 
1.8%
TensorFlow35
 
1.5%
TensorFlow;Torch/PyTorch33
 
1.5%
Other values (773)1480
65.5%
(Missing)307
 
13.6%

Length

2022-08-24T16:41:27.636031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apache457
 
13.2%
net;.net153
 
4.4%
net150
 
4.3%
3d122
 
3.5%
engine117
 
3.4%
3d;unreal101
 
2.9%
pandas70
 
2.0%
keras;pandas;tensorflow;torch/pytorch66
 
1.9%
ansible;apache54
 
1.6%
core;apache54
 
1.6%
Other values (636)2106
61.0%

Most occurring characters

ValueCountFrequency (%)
o6282
 
9.1%
a5676
 
8.2%
r5573
 
8.0%
;5552
 
8.0%
e4832
 
7.0%
s3802
 
5.5%
T3764
 
5.4%
n3258
 
4.7%
c2636
 
3.8%
h2452
 
3.5%
Other values (32)25430
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter46987
67.8%
Uppercase Letter13047
 
18.8%
Other Punctuation7487
 
10.8%
Space Separator1496
 
2.2%
Decimal Number240
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o6282
13.4%
a5676
12.1%
r5573
11.9%
e4832
10.3%
s3802
8.1%
n3258
 
6.9%
c2636
 
5.6%
h2452
 
5.2%
d2134
 
4.5%
p1746
 
3.7%
Other values (13)8596
18.3%
Uppercase Letter
ValueCountFrequency (%)
T3764
28.8%
P2090
16.0%
F1403
 
10.8%
N1265
 
9.7%
K768
 
5.9%
A749
 
5.7%
E644
 
4.9%
S585
 
4.5%
H450
 
3.4%
C385
 
3.0%
Other values (4)944
 
7.2%
Other Punctuation
ValueCountFrequency (%)
;5552
74.2%
.1028
 
13.7%
/907
 
12.1%
Space Separator
ValueCountFrequency (%)
1496
100.0%
Decimal Number
ValueCountFrequency (%)
3240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin60034
86.7%
Common9223
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o6282
 
10.5%
a5676
 
9.5%
r5573
 
9.3%
e4832
 
8.0%
s3802
 
6.3%
T3764
 
6.3%
n3258
 
5.4%
c2636
 
4.4%
h2452
 
4.1%
d2134
 
3.6%
Other values (27)19625
32.7%
Common
ValueCountFrequency (%)
;5552
60.2%
1496
 
16.2%
.1028
 
11.1%
/907
 
9.8%
3240
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII69257
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o6282
 
9.1%
a5676
 
8.2%
r5573
 
8.0%
;5552
 
8.0%
e4832
 
7.0%
s3802
 
5.5%
T3764
 
5.4%
n3258
 
4.7%
c2636
 
3.8%
h2452
 
3.5%
Other values (32)25430
36.7%

misc_tech_worked_with
Categorical

HIGH CARDINALITY
MISSING

Distinct578
Distinct (%)29.4%
Missing297
Missing (%)13.1%
Memory size17.8 KiB
Pandas
223 
Keras;Pandas;TensorFlow
 
97
Keras;Pandas;TensorFlow;Torch/PyTorch
 
72
Node.js
 
55
Pandas;TensorFlow
 
50
Other values (573)
1467 

Length

Max length140
Median length82
Mean length27.00763747
Min length4

Characters and Unicode

Total characters53043
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique402 ?
Unique (%)20.5%

Sample

1st rowNode.js;Pandas
2nd row.NET;.NET Core
3rd rowKeras;Pandas;TensorFlow
4th rowKeras;Pandas;TensorFlow
5th rowNode.js;Pandas

Common Values

ValueCountFrequency (%)
Pandas223
 
9.9%
Keras;Pandas;TensorFlow97
 
4.3%
Keras;Pandas;TensorFlow;Torch/PyTorch72
 
3.2%
Node.js55
 
2.4%
Pandas;TensorFlow50
 
2.2%
Node.js;Pandas43
 
1.9%
.NET37
 
1.6%
.NET;.NET Core35
 
1.5%
Keras;TensorFlow33
 
1.5%
TensorFlow32
 
1.4%
Other values (568)1287
56.9%
(Missing)297
 
13.1%

Length

2022-08-24T16:41:27.859842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apache296
 
9.9%
net;.net246
 
8.3%
pandas223
 
7.5%
keras;pandas;tensorflow97
 
3.3%
3d91
 
3.1%
net91
 
3.1%
keras;pandas;tensorflow;torch/pytorch72
 
2.4%
node.js55
 
1.8%
pandas;tensorflow50
 
1.7%
core46
 
1.5%
Other values (497)1713
57.5%

Most occurring characters

ValueCountFrequency (%)
a4929
 
9.3%
o4645
 
8.8%
;4239
 
8.0%
r3782
 
7.1%
e3677
 
6.9%
s3572
 
6.7%
T2735
 
5.2%
n2666
 
5.0%
d2229
 
4.2%
P1793
 
3.4%
Other values (32)18776
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter35387
66.7%
Uppercase Letter10506
 
19.8%
Other Punctuation6000
 
11.3%
Space Separator1016
 
1.9%
Decimal Number134
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a4929
13.9%
o4645
13.1%
r3782
10.7%
e3677
10.4%
s3572
10.1%
n2666
7.5%
d2229
 
6.3%
c1523
 
4.3%
h1414
 
4.0%
l1198
 
3.4%
Other values (13)5752
16.3%
Uppercase Letter
ValueCountFrequency (%)
T2735
26.0%
P1793
17.1%
N1412
13.4%
F1007
 
9.6%
E731
 
7.0%
K658
 
6.3%
A534
 
5.1%
C404
 
3.8%
S390
 
3.7%
H322
 
3.1%
Other values (4)520
 
4.9%
Other Punctuation
ValueCountFrequency (%)
;4239
70.7%
.1267
 
21.1%
/494
 
8.2%
Space Separator
ValueCountFrequency (%)
1016
100.0%
Decimal Number
ValueCountFrequency (%)
3134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin45893
86.5%
Common7150
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a4929
 
10.7%
o4645
 
10.1%
r3782
 
8.2%
e3677
 
8.0%
s3572
 
7.8%
T2735
 
6.0%
n2666
 
5.8%
d2229
 
4.9%
P1793
 
3.9%
c1523
 
3.3%
Other values (27)14342
31.3%
Common
ValueCountFrequency (%)
;4239
59.3%
.1267
 
17.7%
1016
 
14.2%
/494
 
6.9%
3134
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII53043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a4929
 
9.3%
o4645
 
8.8%
;4239
 
8.0%
r3782
 
7.1%
e3677
 
6.9%
s3572
 
6.7%
T2735
 
5.2%
n2666
 
5.0%
d2229
 
4.2%
P1793
 
3.4%
Other values (32)18776
35.4%

new_collab_tools_desire_next_year
Categorical

HIGH CARDINALITY
MISSING

Distinct402
Distinct (%)19.9%
Missing238
Missing (%)10.5%
Memory size17.8 KiB
Github
 
153
Github;Slack;Google Suite (Docs, Meet, etc)
 
104
Github;Gitlab
 
87
Github;Slack
 
77
Github;Google Suite (Docs, Meet, etc)
 
57
Other values (397)
1545 

Length

Max length149
Median length114
Mean length37.13049926
Min length4

Characters and Unicode

Total characters75115
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)9.9%

Sample

1st rowConfluence;Jira;Github;Slack;Trello
2nd rowGithub;Gitlab
3rd rowGithub;Slack;Google Suite (Docs, Meet, etc)
4th rowJira
5th rowGitlab

Common Values

ValueCountFrequency (%)
Github153
 
6.8%
Github;Slack;Google Suite (Docs, Meet, etc)104
 
4.6%
Github;Gitlab87
 
3.8%
Github;Slack77
 
3.4%
Github;Google Suite (Docs, Meet, etc)57
 
2.5%
Github;Gitlab;Slack57
 
2.5%
Github;Gitlab;Slack;Google Suite (Docs, Meet, etc)42
 
1.9%
Github;Gitlab;Google Suite (Docs, Meet, etc)36
 
1.6%
Gitlab35
 
1.5%
Confluence;Jira;Github;Slack;Google Suite (Docs, Meet, etc)29
 
1.3%
Other values (392)1346
59.5%
(Missing)238
 
10.5%

Length

2022-08-24T16:41:28.093237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
suite826
 
12.2%
docs826
 
12.2%
meet826
 
12.2%
etc699
 
10.3%
teams352
 
5.2%
for240
 
3.5%
overflow240
 
3.5%
teams;microsoft154
 
2.3%
github153
 
2.3%
azure140
 
2.1%
Other values (190)2308
34.1%

Most occurring characters

ValueCountFrequency (%)
e6616
 
8.8%
t5837
 
7.8%
o5264
 
7.0%
4741
 
6.3%
;4699
 
6.3%
i4602
 
6.1%
l4153
 
5.5%
c4081
 
5.4%
a3382
 
4.5%
G3275
 
4.4%
Other values (27)28465
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter51974
69.2%
Uppercase Letter10397
 
13.8%
Other Punctuation6351
 
8.5%
Space Separator4741
 
6.3%
Open Punctuation826
 
1.1%
Close Punctuation826
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6616
12.7%
t5837
11.2%
o5264
10.1%
i4602
8.9%
l4153
8.0%
c4081
7.9%
a3382
 
6.5%
u3219
 
6.2%
r2588
 
5.0%
b2496
 
4.8%
Other values (11)9736
18.7%
Uppercase Letter
ValueCountFrequency (%)
G3275
31.5%
S2074
19.9%
M1496
14.4%
T992
 
9.5%
D826
 
7.9%
J657
 
6.3%
C417
 
4.0%
A326
 
3.1%
O240
 
2.3%
F47
 
0.5%
Other Punctuation
ValueCountFrequency (%)
;4699
74.0%
,1652
 
26.0%
Space Separator
ValueCountFrequency (%)
4741
100.0%
Open Punctuation
ValueCountFrequency (%)
(826
100.0%
Close Punctuation
ValueCountFrequency (%)
)826
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin62371
83.0%
Common12744
 
17.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6616
 
10.6%
t5837
 
9.4%
o5264
 
8.4%
i4602
 
7.4%
l4153
 
6.7%
c4081
 
6.5%
a3382
 
5.4%
G3275
 
5.3%
u3219
 
5.2%
r2588
 
4.1%
Other values (22)19354
31.0%
Common
ValueCountFrequency (%)
4741
37.2%
;4699
36.9%
,1652
 
13.0%
(826
 
6.5%
)826
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII75115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6616
 
8.8%
t5837
 
7.8%
o5264
 
7.0%
4741
 
6.3%
;4699
 
6.3%
i4602
 
6.1%
l4153
 
5.5%
c4081
 
5.4%
a3382
 
4.5%
G3275
 
4.4%
Other values (27)28465
37.9%

new_collab_tools_worked_with
Categorical

HIGH CARDINALITY
MISSING

Distinct406
Distinct (%)18.5%
Missing69
Missing (%)3.1%
Memory size17.8 KiB
Github
 
96
Github;Slack;Google Suite (Docs, Meet, etc)
 
76
Confluence;Jira;Github;Slack;Google Suite (Docs, Meet, etc)
 
71
Github;Gitlab
 
53
Github;Google Suite (Docs, Meet, etc)
 
49
Other values (401)
1847 

Length

Max length149
Median length103
Mean length45.07664234
Min length4

Characters and Unicode

Total characters98808
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)7.8%

Sample

1st rowConfluence;Jira;Github;Slack;Trello
2nd rowConfluence;Jira;Github;Gitlab;Microsoft Azure;Google Suite (Docs, Meet, etc)
3rd rowConfluence;Jira;Github;Slack;Google Suite (Docs, Meet, etc)
4th rowJira
5th rowGithub;Slack

Common Values

ValueCountFrequency (%)
Github96
 
4.2%
Github;Slack;Google Suite (Docs, Meet, etc)76
 
3.4%
Confluence;Jira;Github;Slack;Google Suite (Docs, Meet, etc)71
 
3.1%
Github;Gitlab53
 
2.3%
Github;Google Suite (Docs, Meet, etc)49
 
2.2%
Github;Gitlab;Slack;Google Suite (Docs, Meet, etc)47
 
2.1%
Github;Slack43
 
1.9%
Github;Slack;Trello;Google Suite (Docs, Meet, etc)42
 
1.9%
Confluence;Jira;Github;Gitlab;Slack;Google Suite (Docs, Meet, etc)41
 
1.8%
Github;Gitlab;Slack;Trello;Google Suite (Docs, Meet, etc)37
 
1.6%
Other values (396)1637
72.4%
(Missing)69
 
3.1%

Length

2022-08-24T16:41:28.343142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
suite1115
13.9%
docs1115
13.9%
meet1115
13.9%
etc1037
 
12.9%
teams298
 
3.7%
teams;microsoft223
 
2.8%
azure134
 
1.7%
overflow114
 
1.4%
for114
 
1.4%
teams;google110
 
1.4%
Other values (178)2674
33.2%

Most occurring characters

ValueCountFrequency (%)
e9177
 
9.3%
t7243
 
7.3%
o7210
 
7.3%
;6785
 
6.9%
i5995
 
6.1%
5857
 
5.9%
l5656
 
5.7%
c5605
 
5.7%
a4344
 
4.4%
u4158
 
4.2%
Other values (27)36778
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter68101
68.9%
Uppercase Letter13605
 
13.8%
Other Punctuation9015
 
9.1%
Space Separator5857
 
5.9%
Open Punctuation1115
 
1.1%
Close Punctuation1115
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e9177
13.5%
t7243
10.6%
o7210
10.6%
i5995
8.8%
l5656
8.3%
c5605
8.2%
a4344
 
6.4%
u4158
 
6.1%
r3387
 
5.0%
b2875
 
4.2%
Other values (11)12451
18.3%
Uppercase Letter
ValueCountFrequency (%)
G3917
28.8%
S2570
18.9%
M2097
15.4%
T1412
 
10.4%
D1115
 
8.2%
J1096
 
8.1%
C792
 
5.8%
A346
 
2.5%
O114
 
0.8%
F73
 
0.5%
Other Punctuation
ValueCountFrequency (%)
;6785
75.3%
,2230
 
24.7%
Space Separator
ValueCountFrequency (%)
5857
100.0%
Open Punctuation
ValueCountFrequency (%)
(1115
100.0%
Close Punctuation
ValueCountFrequency (%)
)1115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin81706
82.7%
Common17102
 
17.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e9177
 
11.2%
t7243
 
8.9%
o7210
 
8.8%
i5995
 
7.3%
l5656
 
6.9%
c5605
 
6.9%
a4344
 
5.3%
u4158
 
5.1%
G3917
 
4.8%
r3387
 
4.1%
Other values (22)25014
30.6%
Common
ValueCountFrequency (%)
;6785
39.7%
5857
34.2%
,2230
 
13.0%
(1115
 
6.5%
)1115
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII98808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e9177
 
9.3%
t7243
 
7.3%
o7210
 
7.3%
;6785
 
6.9%
i5995
 
6.1%
5857
 
5.9%
l5656
 
5.7%
c5605
 
5.7%
a4344
 
4.4%
u4158
 
4.2%
Other values (27)36778
37.2%

new_dev_ops
Categorical

Distinct3
Distinct (%)0.1%
Missing16
Missing (%)0.7%
Memory size17.8 KiB
Yes
973 
No
917 
Not sure
355 

Length

Max length8
Median length3
Mean length3.382182628
Min length2

Characters and Unicode

Total characters7593
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot sure
2nd rowYes
3rd rowYes
4th rowNot sure
5th rowNo

Common Values

ValueCountFrequency (%)
Yes973
43.0%
No917
40.6%
Not sure355
 
15.7%
(Missing)16
 
0.7%

Length

2022-08-24T16:41:28.528370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:28.674724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
yes973
37.4%
no917
35.3%
not355
 
13.7%
sure355
 
13.7%

Most occurring characters

ValueCountFrequency (%)
e1328
17.5%
s1328
17.5%
N1272
16.8%
o1272
16.8%
Y973
12.8%
t355
 
4.7%
355
 
4.7%
u355
 
4.7%
r355
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4993
65.8%
Uppercase Letter2245
29.6%
Space Separator355
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1328
26.6%
s1328
26.6%
o1272
25.5%
t355
 
7.1%
u355
 
7.1%
r355
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
N1272
56.7%
Y973
43.3%
Space Separator
ValueCountFrequency (%)
355
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7238
95.3%
Common355
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1328
18.3%
s1328
18.3%
N1272
17.6%
o1272
17.6%
Y973
13.4%
t355
 
4.9%
u355
 
4.9%
r355
 
4.9%
Common
ValueCountFrequency (%)
355
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7593
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1328
17.5%
s1328
17.5%
N1272
16.8%
o1272
16.8%
Y973
12.8%
t355
 
4.7%
355
 
4.7%
u355
 
4.7%
r355
 
4.7%

new_dev_ops_impt
Categorical

MISSING

Distinct5
Distinct (%)0.2%
Missing78
Missing (%)3.4%
Memory size17.8 KiB
Extremely important
1038 
Somewhat important
662 
Neutral
406 
Not very important
 
47
Not at all important
 
30

Length

Max length20
Median length19
Mean length16.45716903
Min length7

Characters and Unicode

Total characters35926
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutral
2nd rowNeutral
3rd rowExtremely important
4th rowNeutral
5th rowSomewhat important

Common Values

ValueCountFrequency (%)
Extremely important1038
45.9%
Somewhat important662
29.3%
Neutral406
 
18.0%
Not very important47
 
2.1%
Not at all important30
 
1.3%
(Missing)78
 
3.4%

Length

2022-08-24T16:41:28.815316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:29.006170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
important1777
43.7%
extremely1038
25.5%
somewhat662
 
16.3%
neutral406
 
10.0%
not77
 
1.9%
very47
 
1.2%
at30
 
0.7%
all30
 
0.7%

Most occurring characters

ValueCountFrequency (%)
t5767
16.1%
m3477
9.7%
r3268
9.1%
e3191
8.9%
a2905
 
8.1%
o2516
 
7.0%
1884
 
5.2%
p1777
 
4.9%
i1777
 
4.9%
n1777
 
4.9%
Other values (10)7587
21.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31859
88.7%
Uppercase Letter2183
 
6.1%
Space Separator1884
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t5767
18.1%
m3477
10.9%
r3268
10.3%
e3191
10.0%
a2905
9.1%
o2516
7.9%
p1777
 
5.6%
i1777
 
5.6%
n1777
 
5.6%
l1504
 
4.7%
Other values (6)3900
12.2%
Uppercase Letter
ValueCountFrequency (%)
E1038
47.5%
S662
30.3%
N483
22.1%
Space Separator
ValueCountFrequency (%)
1884
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin34042
94.8%
Common1884
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t5767
16.9%
m3477
10.2%
r3268
9.6%
e3191
9.4%
a2905
8.5%
o2516
7.4%
p1777
 
5.2%
i1777
 
5.2%
n1777
 
5.2%
l1504
 
4.4%
Other values (9)6083
17.9%
Common
ValueCountFrequency (%)
1884
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t5767
16.1%
m3477
9.7%
r3268
9.1%
e3191
8.9%
a2905
 
8.1%
o2516
 
7.0%
1884
 
5.2%
p1777
 
4.9%
i1777
 
4.9%
n1777
 
4.9%
Other values (10)7587
21.1%

new_ed_impt
Categorical

Distinct5
Distinct (%)0.2%
Missing15
Missing (%)0.7%
Memory size17.8 KiB
Very important
663 
Fairly important
550 
Critically important
467 
Somewhat important
360 
Not at all important/not necessary
206 

Length

Max length34
Median length20
Mean length18.2128228
Min length14

Characters and Unicode

Total characters40906
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSomewhat important
2nd rowNot at all important/not necessary
3rd rowVery important
4th rowCritically important
5th rowVery important

Common Values

ValueCountFrequency (%)
Very important663
29.3%
Fairly important550
24.3%
Critically important467
20.7%
Somewhat important360
15.9%
Not at all important/not necessary206
 
9.1%
(Missing)15
 
0.7%

Length

2022-08-24T16:41:29.197599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:29.377342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
important2040
39.9%
very663
 
13.0%
fairly550
 
10.8%
critically467
 
9.1%
somewhat360
 
7.0%
not206
 
4.0%
at206
 
4.0%
all206
 
4.0%
important/not206
 
4.0%
necessary206
 
4.0%

Most occurring characters

ValueCountFrequency (%)
t5937
14.5%
a4241
10.4%
r4132
10.1%
i3730
9.1%
o3018
 
7.4%
2864
 
7.0%
n2658
 
6.5%
m2606
 
6.4%
p2246
 
5.5%
l1896
 
4.6%
Other values (12)7578
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter35590
87.0%
Space Separator2864
 
7.0%
Uppercase Letter2246
 
5.5%
Other Punctuation206
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t5937
16.7%
a4241
11.9%
r4132
11.6%
i3730
10.5%
o3018
8.5%
n2658
7.5%
m2606
7.3%
p2246
 
6.3%
l1896
 
5.3%
y1886
 
5.3%
Other values (5)3240
9.1%
Uppercase Letter
ValueCountFrequency (%)
V663
29.5%
F550
24.5%
C467
20.8%
S360
16.0%
N206
 
9.2%
Space Separator
ValueCountFrequency (%)
2864
100.0%
Other Punctuation
ValueCountFrequency (%)
/206
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin37836
92.5%
Common3070
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t5937
15.7%
a4241
11.2%
r4132
10.9%
i3730
9.9%
o3018
8.0%
n2658
7.0%
m2606
6.9%
p2246
 
5.9%
l1896
 
5.0%
y1886
 
5.0%
Other values (10)5486
14.5%
Common
ValueCountFrequency (%)
2864
93.3%
/206
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII40906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t5937
14.5%
a4241
10.4%
r4132
10.1%
i3730
9.1%
o3018
 
7.4%
2864
 
7.0%
n2658
 
6.5%
m2606
 
6.4%
p2246
 
5.5%
l1896
 
4.6%
Other values (12)7578
18.5%

new_job_hunt
Categorical

HIGH CARDINALITY
MISSING

Distinct697
Distinct (%)31.8%
Missing70
Missing (%)3.1%
Memory size17.8 KiB
Curious about other opportunities;Better compensation;Wanting to work with new technologies;Growth or leadership opportunities
 
60
Curious about other opportunities;Better compensation;Better work/life balance;Wanting to work with new technologies;Growth or leadership opportunities
 
50
Curious about other opportunities;Better compensation;Growth or leadership opportunities
 
45
Curious about other opportunities
 
40
Curious about other opportunities;Better compensation;Better work/life balance;Wanting to work with new technologies;Growth or leadership opportunities;Looking to relocate
 
39
Other values (692)
1957 

Length

Max length377
Median length257
Mean length130.9566408
Min length12

Characters and Unicode

Total characters286926
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique393 ?
Unique (%)17.9%

Sample

1st rowHaving a bad day (or week or month) at work;Curious about other opportunities;Wanting to work with new technologies;Growth or leadership opportunities
2nd rowJust because;Having a bad day (or week or month) at work;Curious about other opportunities;Wanting to work with new technologies;Looking to relocate
3rd rowCurious about other opportunities;Better compensation;Trouble with leadership at my company;Wanting to work with new technologies;Growth or leadership opportunities;Looking to relocate
4th rowBetter work/life balance
5th rowCurious about other opportunities;Wanting to work with new technologies;Growth or leadership opportunities

Common Values

ValueCountFrequency (%)
Curious about other opportunities;Better compensation;Wanting to work with new technologies;Growth or leadership opportunities60
 
2.7%
Curious about other opportunities;Better compensation;Better work/life balance;Wanting to work with new technologies;Growth or leadership opportunities50
 
2.2%
Curious about other opportunities;Better compensation;Growth or leadership opportunities45
 
2.0%
Curious about other opportunities40
 
1.8%
Curious about other opportunities;Better compensation;Better work/life balance;Wanting to work with new technologies;Growth or leadership opportunities;Looking to relocate39
 
1.7%
Curious about other opportunities;Better compensation;Wanting to work with new technologies38
 
1.7%
Better compensation31
 
1.4%
Curious about other opportunities;Wanting to work with new technologies;Growth or leadership opportunities28
 
1.2%
Better compensation;Wanting to work with new technologies;Growth or leadership opportunities28
 
1.2%
Curious about other opportunities;Better compensation26
 
1.1%
Other values (687)1806
79.9%
(Missing)70
 
3.1%

Length

2022-08-24T16:41:29.674725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
with2697
 
8.2%
or2254
 
6.9%
to2143
 
6.5%
leadership1917
 
5.8%
about1412
 
4.3%
other1412
 
4.3%
work1257
 
3.8%
new1249
 
3.8%
my1184
 
3.6%
opportunities;better1129
 
3.4%
Other values (72)16233
49.4%

Most occurring characters

ValueCountFrequency (%)
30696
 
10.7%
o28424
 
9.9%
t27738
 
9.7%
e26656
 
9.3%
r19651
 
6.8%
i18808
 
6.6%
n15416
 
5.4%
a14811
 
5.2%
s10570
 
3.7%
p9817
 
3.4%
Other values (25)84339
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter237684
82.8%
Space Separator30696
 
10.7%
Uppercase Letter9413
 
3.3%
Other Punctuation8209
 
2.9%
Open Punctuation462
 
0.2%
Close Punctuation462
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o28424
12.0%
t27738
11.7%
e26656
11.2%
r19651
 
8.3%
i18808
 
7.9%
n15416
 
6.5%
a14811
 
6.2%
s10570
 
4.4%
p9817
 
4.1%
h9595
 
4.0%
Other values (12)56198
23.6%
Uppercase Letter
ValueCountFrequency (%)
B2552
27.1%
W1513
16.1%
C1412
15.0%
G1330
14.1%
T1184
12.6%
L630
 
6.7%
H462
 
4.9%
J330
 
3.5%
Other Punctuation
ValueCountFrequency (%)
;7222
88.0%
/987
 
12.0%
Space Separator
ValueCountFrequency (%)
30696
100.0%
Open Punctuation
ValueCountFrequency (%)
(462
100.0%
Close Punctuation
ValueCountFrequency (%)
)462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin247097
86.1%
Common39829
 
13.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o28424
11.5%
t27738
11.2%
e26656
10.8%
r19651
 
8.0%
i18808
 
7.6%
n15416
 
6.2%
a14811
 
6.0%
s10570
 
4.3%
p9817
 
4.0%
h9595
 
3.9%
Other values (20)65611
26.6%
Common
ValueCountFrequency (%)
30696
77.1%
;7222
 
18.1%
/987
 
2.5%
(462
 
1.2%
)462
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII286926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30696
 
10.7%
o28424
 
9.9%
t27738
 
9.7%
e26656
 
9.3%
r19651
 
6.8%
i18808
 
6.6%
n15416
 
5.4%
a14811
 
5.2%
s10570
 
3.7%
p9817
 
3.4%
Other values (25)84339
29.4%

new_job_hunt_research
Categorical

HIGH CARDINALITY
MISSING

Distinct62
Distinct (%)2.9%
Missing112
Missing (%)5.0%
Memory size17.8 KiB
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company
189 
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family
 
134
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Read other media like news articles, founder profiles, etc. about the company
 
101
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company
 
92
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company
 
88
Other values (57)
1545 

Length

Max length362
Median length247
Mean length199.6351792
Min length36

Characters and Unicode

Total characters429016
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowRead company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Directly asking current or past employees at the company
2nd rowRead company media, such as employee blogs or company culture videos;Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company
3rd rowPersonal network - friends or family;Directly asking current or past employees at the company
4th rowRead company media, such as employee blogs or company culture videos;Personal network - friends or family
5th rowPublicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company

Common Values

ValueCountFrequency (%)
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company189
 
8.4%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family134
 
5.9%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Read other media like news articles, founder profiles, etc. about the company101
 
4.5%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company92
 
4.1%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company88
 
3.9%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Directly asking current or past employees at the company76
 
3.4%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company76
 
3.4%
Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Directly asking current or past employees at the company75
 
3.3%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind)73
 
3.2%
Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company66
 
2.9%
Other values (52)1179
52.1%
(Missing)112
 
5.0%

Length

2022-08-24T16:41:30.019380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
company4887
 
8.2%
or3804
 
6.4%
media2584
 
4.4%
e.g2195
 
3.7%
the2091
 
3.5%
party1522
 
2.6%
sites1522
 
2.6%
glassdoor1522
 
2.6%
reviews1522
 
2.6%
from1522
 
2.6%
Other values (48)36230
61.0%

Most occurring characters

ValueCountFrequency (%)
57252
 
13.3%
e39626
 
9.2%
o28930
 
6.7%
a28819
 
6.7%
r26000
 
6.1%
s23150
 
5.4%
i22398
 
5.2%
n19901
 
4.6%
l18933
 
4.4%
t18573
 
4.3%
Other values (25)145434
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter339187
79.1%
Space Separator57252
 
13.3%
Other Punctuation15844
 
3.7%
Uppercase Letter10882
 
2.5%
Open Punctuation2195
 
0.5%
Close Punctuation2195
 
0.5%
Dash Punctuation1461
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e39626
11.7%
o28930
 
8.5%
a28819
 
8.5%
r26000
 
7.7%
s23150
 
6.8%
i22398
 
6.6%
n19901
 
5.9%
l18933
 
5.6%
t18573
 
5.5%
m15032
 
4.4%
Other values (12)97825
28.8%
Uppercase Letter
ValueCountFrequency (%)
R2584
23.7%
C2195
20.2%
P2134
19.6%
G1522
14.0%
B1522
14.0%
D925
 
8.5%
Other Punctuation
ValueCountFrequency (%)
.5556
35.1%
,5272
33.3%
;5016
31.7%
Space Separator
ValueCountFrequency (%)
57252
100.0%
Open Punctuation
ValueCountFrequency (%)
(2195
100.0%
Close Punctuation
ValueCountFrequency (%)
)2195
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1461
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin350069
81.6%
Common78947
 
18.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e39626
 
11.3%
o28930
 
8.3%
a28819
 
8.2%
r26000
 
7.4%
s23150
 
6.6%
i22398
 
6.4%
n19901
 
5.7%
l18933
 
5.4%
t18573
 
5.3%
m15032
 
4.3%
Other values (18)108707
31.1%
Common
ValueCountFrequency (%)
57252
72.5%
.5556
 
7.0%
,5272
 
6.7%
;5016
 
6.4%
(2195
 
2.8%
)2195
 
2.8%
-1461
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII429016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57252
 
13.3%
e39626
 
9.2%
o28930
 
6.7%
a28819
 
6.7%
r26000
 
6.1%
s23150
 
5.4%
i22398
 
5.2%
n19901
 
4.6%
l18933
 
4.4%
t18573
 
4.3%
Other values (25)145434
33.9%

new_learn
Categorical

MISSING

Distinct4
Distinct (%)0.2%
Missing38
Missing (%)1.7%
Memory size17.8 KiB
Every few months
847 
Once a year
811 
Once every few years
540 
Once a decade
 
25

Length

Max length20
Median length16
Mean length15.11381017
Min length11

Characters and Unicode

Total characters33598
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEvery few months
2nd rowOnce a year
3rd rowOnce a year
4th rowOnce every few years
5th rowOnce a year

Common Values

ValueCountFrequency (%)
Every few months847
37.5%
Once a year811
35.9%
Once every few years540
23.9%
Once a decade25
 
1.1%
(Missing)38
 
1.7%

Length

2022-08-24T16:41:30.317720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:30.552040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
every1387
19.2%
few1387
19.2%
once1376
19.1%
months847
11.7%
a836
11.6%
year811
11.2%
years540
 
7.5%
decade25
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e6091
18.1%
4986
14.8%
r2738
 
8.1%
y2738
 
8.1%
n2223
 
6.6%
a2212
 
6.6%
c1401
 
4.2%
s1387
 
4.1%
f1387
 
4.1%
w1387
 
4.1%
Other values (8)7048
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter26389
78.5%
Space Separator4986
 
14.8%
Uppercase Letter2223
 
6.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6091
23.1%
r2738
10.4%
y2738
10.4%
n2223
 
8.4%
a2212
 
8.4%
c1401
 
5.3%
s1387
 
5.3%
f1387
 
5.3%
w1387
 
5.3%
v1387
 
5.3%
Other values (5)3438
13.0%
Uppercase Letter
ValueCountFrequency (%)
O1376
61.9%
E847
38.1%
Space Separator
ValueCountFrequency (%)
4986
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28612
85.2%
Common4986
 
14.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6091
21.3%
r2738
9.6%
y2738
9.6%
n2223
 
7.8%
a2212
 
7.7%
c1401
 
4.9%
s1387
 
4.8%
f1387
 
4.8%
w1387
 
4.8%
v1387
 
4.8%
Other values (7)5661
19.8%
Common
ValueCountFrequency (%)
4986
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII33598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6091
18.1%
4986
14.8%
r2738
 
8.1%
y2738
 
8.1%
n2223
 
6.6%
a2212
 
6.6%
c1401
 
4.2%
s1387
 
4.1%
f1387
 
4.1%
w1387
 
4.1%
Other values (8)7048
21.0%

new_off_topic
Categorical

MISSING

Distinct3
Distinct (%)0.1%
Missing106
Missing (%)4.7%
Memory size17.8 KiB
No
877 
Not sure
813 
Yes
465 

Length

Max length8
Median length3
Mean length4.479350348
Min length2

Characters and Unicode

Total characters9653
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No877
38.8%
Not sure813
36.0%
Yes465
20.6%
(Missing)106
 
4.7%

Length

2022-08-24T16:41:30.756726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:30.897315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no877
29.5%
not813
27.4%
sure813
27.4%
yes465
15.7%

Most occurring characters

ValueCountFrequency (%)
N1690
17.5%
o1690
17.5%
s1278
13.2%
e1278
13.2%
t813
8.4%
813
8.4%
u813
8.4%
r813
8.4%
Y465
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6685
69.3%
Uppercase Letter2155
 
22.3%
Space Separator813
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1690
25.3%
s1278
19.1%
e1278
19.1%
t813
12.2%
u813
12.2%
r813
12.2%
Uppercase Letter
ValueCountFrequency (%)
N1690
78.4%
Y465
 
21.6%
Space Separator
ValueCountFrequency (%)
813
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8840
91.6%
Common813
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1690
19.1%
o1690
19.1%
s1278
14.5%
e1278
14.5%
t813
9.2%
u813
9.2%
r813
9.2%
Y465
 
5.3%
Common
ValueCountFrequency (%)
813
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9653
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N1690
17.5%
o1690
17.5%
s1278
13.2%
e1278
13.2%
t813
8.4%
813
8.4%
u813
8.4%
r813
8.4%
Y465
 
4.8%

new_onboard_good
Categorical

Distinct3
Distinct (%)0.1%
Missing17
Missing (%)0.8%
Memory size17.8 KiB
Yes
1062 
No
653 
Onboarding? What onboarding?
529 

Length

Max length28
Median length3
Mean length8.602495544
Min length2

Characters and Unicode

Total characters19304
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowOnboarding? What onboarding?
3rd rowNo
4th rowYes
5th rowOnboarding? What onboarding?

Common Values

ValueCountFrequency (%)
Yes1062
47.0%
No653
28.9%
Onboarding? What onboarding?529
23.4%
(Missing)17
 
0.8%

Length

2022-08-24T16:41:31.046574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:31.187166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
yes1062
32.2%
onboarding1058
32.0%
no653
19.8%
what529
16.0%

Most occurring characters

ValueCountFrequency (%)
o2240
 
11.6%
n2116
 
11.0%
a1587
 
8.2%
Y1062
 
5.5%
s1062
 
5.5%
e1062
 
5.5%
i1058
 
5.5%
1058
 
5.5%
?1058
 
5.5%
g1058
 
5.5%
Other values (8)5943
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14415
74.7%
Uppercase Letter2773
 
14.4%
Space Separator1058
 
5.5%
Other Punctuation1058
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o2240
15.5%
n2116
14.7%
a1587
11.0%
s1062
7.4%
e1062
7.4%
i1058
7.3%
g1058
7.3%
r1058
7.3%
d1058
7.3%
b1058
7.3%
Other values (2)1058
7.3%
Uppercase Letter
ValueCountFrequency (%)
Y1062
38.3%
N653
23.5%
O529
19.1%
W529
19.1%
Space Separator
ValueCountFrequency (%)
1058
100.0%
Other Punctuation
ValueCountFrequency (%)
?1058
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17188
89.0%
Common2116
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o2240
13.0%
n2116
12.3%
a1587
9.2%
Y1062
 
6.2%
s1062
 
6.2%
e1062
 
6.2%
i1058
 
6.2%
g1058
 
6.2%
r1058
 
6.2%
d1058
 
6.2%
Other values (6)3827
22.3%
Common
ValueCountFrequency (%)
1058
50.0%
?1058
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII19304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o2240
 
11.6%
n2116
 
11.0%
a1587
 
8.2%
Y1062
 
5.5%
s1062
 
5.5%
e1062
 
5.5%
i1058
 
5.5%
1058
 
5.5%
?1058
 
5.5%
g1058
 
5.5%
Other values (8)5943
30.8%
Distinct2
Distinct (%)0.1%
Missing4
Missing (%)0.2%
Memory size4.5 KiB
False
1312 
True
945 
(Missing)
 
4
ValueCountFrequency (%)
False1312
58.0%
True945
41.8%
(Missing)4
 
0.2%
2022-08-24T16:41:31.342584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

new_overtime
Categorical

Distinct5
Distinct (%)0.2%
Missing6
Missing (%)0.3%
Memory size17.8 KiB
Often: 1-2 days per week or more
706 
Sometimes: 1-2 days per month but less than weekly
642 
Occasionally: 1-2 days per quarter but less than monthly
413 
Rarely: 1-2 days per year or less
290 
Never
204 

Length

Max length56
Median length50
Mean length39.20620843
Min length5

Characters and Unicode

Total characters88410
Distinct characters31
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes: 1-2 days per month but less than weekly
2nd rowOccasionally: 1-2 days per quarter but less than monthly
3rd rowSometimes: 1-2 days per month but less than weekly
4th rowOccasionally: 1-2 days per quarter but less than monthly
5th rowRarely: 1-2 days per year or less

Common Values

ValueCountFrequency (%)
Often: 1-2 days per week or more706
31.2%
Sometimes: 1-2 days per month but less than weekly642
28.4%
Occasionally: 1-2 days per quarter but less than monthly413
18.3%
Rarely: 1-2 days per year or less290
12.8%
Never204
 
9.0%
(Missing)6
 
0.3%

Length

2022-08-24T16:41:31.471411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:31.652561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1-22051
12.3%
days2051
12.3%
per2051
12.3%
less1345
 
8.1%
but1055
 
6.3%
than1055
 
6.3%
or996
 
6.0%
often706
 
4.2%
more706
 
4.2%
week706
 
4.2%
Other values (9)3949
23.7%

Most occurring characters

ValueCountFrequency (%)
14416
16.3%
e10189
 
11.5%
s5796
 
6.6%
r5363
 
6.1%
t4926
 
5.6%
a4925
 
5.6%
y4099
 
4.6%
o3812
 
4.3%
l3516
 
4.0%
n3229
 
3.7%
Other values (21)28139
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63535
71.9%
Space Separator14416
 
16.3%
Decimal Number4102
 
4.6%
Uppercase Letter2255
 
2.6%
Dash Punctuation2051
 
2.3%
Other Punctuation2051
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10189
16.0%
s5796
 
9.1%
r5363
 
8.4%
t4926
 
7.8%
a4925
 
7.8%
y4099
 
6.5%
o3812
 
6.0%
l3516
 
5.5%
n3229
 
5.1%
m3045
 
4.8%
Other values (12)14635
23.0%
Uppercase Letter
ValueCountFrequency (%)
O1119
49.6%
S642
28.5%
R290
 
12.9%
N204
 
9.0%
Decimal Number
ValueCountFrequency (%)
22051
50.0%
12051
50.0%
Space Separator
ValueCountFrequency (%)
14416
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2051
100.0%
Other Punctuation
ValueCountFrequency (%)
:2051
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin65790
74.4%
Common22620
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10189
15.5%
s5796
 
8.8%
r5363
 
8.2%
t4926
 
7.5%
a4925
 
7.5%
y4099
 
6.2%
o3812
 
5.8%
l3516
 
5.3%
n3229
 
4.9%
m3045
 
4.6%
Other values (16)16890
25.7%
Common
ValueCountFrequency (%)
14416
63.7%
22051
 
9.1%
-2051
 
9.1%
12051
 
9.1%
:2051
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII88410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14416
16.3%
e10189
 
11.5%
s5796
 
6.6%
r5363
 
6.1%
t4926
 
5.6%
a4925
 
5.6%
y4099
 
4.6%
o3812
 
4.3%
l3516
 
4.0%
n3229
 
3.7%
Other values (21)28139
31.8%

new_purchase_research
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct56
Distinct (%)3.8%
Missing781
Missing (%)34.5%
Memory size17.8 KiB
Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow
321 
Start a free trial;Ask developers I know/work with
188 
Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd
169 
Start a free trial;Visit developer communities like Stack Overflow
100 
Ask developers I know/work with;Visit developer communities like Stack Overflow
98 
Other values (51)
604 

Length

Max length253
Median length213
Mean length101.072973
Min length18

Characters and Unicode

Total characters149588
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.8%

Sample

1st rowStart a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow
2nd rowAsk developers I know/work with;Visit developer communities like Stack Overflow
3rd rowStart a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow
4th rowStart a free trial;Ask developers I know/work with
5th rowStart a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow

Common Values

ValueCountFrequency (%)
Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow321
14.2%
Start a free trial;Ask developers I know/work with188
 
8.3%
Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd169
 
7.5%
Start a free trial;Visit developer communities like Stack Overflow100
 
4.4%
Ask developers I know/work with;Visit developer communities like Stack Overflow98
 
4.3%
Start a free trial79
 
3.5%
Ask developers I know/work with50
 
2.2%
Start a free trial;Ask developers I know/work with;Read ratings or reviews on third party sites like G2Crowd50
 
2.2%
Start a free trial;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd48
 
2.1%
Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd41
 
1.8%
Other values (46)336
14.9%
(Missing)781
34.5%

Length

2022-08-24T16:41:31.857263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
like1488
 
7.0%
i1342
 
6.4%
start1180
 
5.6%
free1180
 
5.6%
a1180
 
5.6%
developers1128
 
5.3%
know/work1128
 
5.3%
developer1011
 
4.8%
communities1011
 
4.8%
stack1011
 
4.8%
Other values (32)9453
44.8%

Most occurring characters

ValueCountFrequency (%)
19632
13.1%
e16421
 
11.0%
r11212
 
7.5%
i11026
 
7.4%
t10887
 
7.3%
o8380
 
5.6%
s7678
 
5.1%
a7572
 
5.1%
l5922
 
4.0%
k5883
 
3.9%
Other values (23)44975
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter117285
78.4%
Space Separator19632
 
13.1%
Uppercase Letter8432
 
5.6%
Other Punctuation3762
 
2.5%
Decimal Number477
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e16421
14.0%
r11212
9.6%
i11026
9.4%
t10887
9.3%
o8380
 
7.1%
s7678
 
6.5%
a7572
 
6.5%
l5922
 
5.0%
k5883
 
5.0%
w5349
 
4.6%
Other values (11)26955
23.0%
Uppercase Letter
ValueCountFrequency (%)
S2191
26.0%
I1342
15.9%
A1128
13.4%
V1011
12.0%
O1011
12.0%
R795
 
9.4%
G477
 
5.7%
C477
 
5.7%
Other Punctuation
ValueCountFrequency (%)
;2634
70.0%
/1128
30.0%
Space Separator
ValueCountFrequency (%)
19632
100.0%
Decimal Number
ValueCountFrequency (%)
2477
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin125717
84.0%
Common23871
 
16.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e16421
13.1%
r11212
 
8.9%
i11026
 
8.8%
t10887
 
8.7%
o8380
 
6.7%
s7678
 
6.1%
a7572
 
6.0%
l5922
 
4.7%
k5883
 
4.7%
w5349
 
4.3%
Other values (19)35387
28.1%
Common
ValueCountFrequency (%)
19632
82.2%
;2634
 
11.0%
/1128
 
4.7%
2477
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII149588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19632
13.1%
e16421
 
11.0%
r11212
 
7.5%
i11026
 
7.4%
t10887
 
7.3%
o8380
 
5.6%
s7678
 
5.1%
a7572
 
5.1%
l5922
 
4.0%
k5883
 
3.9%
Other values (23)44975
30.1%

purple_link
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
Hello, old friend
1225 
Indifferent
405 
Amused
368 
Annoyed
263 

Length

Max length17
Median length17
Mean length12.97169394
Min length6

Characters and Unicode

Total characters29329
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHello, old friend
2nd rowHello, old friend
3rd rowHello, old friend
4th rowAmused
5th rowHello, old friend

Common Values

ValueCountFrequency (%)
Hello, old friend1225
54.2%
Indifferent405
 
17.9%
Amused368
 
16.3%
Annoyed263
 
11.6%

Length

2022-08-24T16:41:32.047219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:32.187848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
hello1225
26.0%
old1225
26.0%
friend1225
26.0%
indifferent405
 
8.6%
amused368
 
7.8%
annoyed263
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e3891
13.3%
l3675
12.5%
d3486
11.9%
o2713
9.3%
n2561
8.7%
2450
8.4%
f2035
6.9%
i1630
5.6%
r1630
5.6%
H1225
 
4.2%
Other values (8)4033
13.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23393
79.8%
Space Separator2450
 
8.4%
Uppercase Letter2261
 
7.7%
Other Punctuation1225
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3891
16.6%
l3675
15.7%
d3486
14.9%
o2713
11.6%
n2561
10.9%
f2035
8.7%
i1630
7.0%
r1630
7.0%
t405
 
1.7%
m368
 
1.6%
Other values (3)999
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
H1225
54.2%
A631
27.9%
I405
 
17.9%
Space Separator
ValueCountFrequency (%)
2450
100.0%
Other Punctuation
ValueCountFrequency (%)
,1225
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25654
87.5%
Common3675
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3891
15.2%
l3675
14.3%
d3486
13.6%
o2713
10.6%
n2561
10.0%
f2035
7.9%
i1630
6.4%
r1630
6.4%
H1225
 
4.8%
A631
 
2.5%
Other values (6)2177
8.5%
Common
ValueCountFrequency (%)
2450
66.7%
,1225
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII29329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3891
13.3%
l3675
12.5%
d3486
11.9%
o2713
9.3%
n2561
8.7%
2450
8.4%
f2035
6.9%
i1630
5.6%
r1630
5.6%
H1225
 
4.2%
Other values (8)4033
13.8%

newso_sites
Categorical

HIGH CORRELATION

Distinct25
Distinct (%)1.1%
Missing4
Missing (%)0.2%
Memory size17.8 KiB
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)
1021 
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)
667 
Stack Overflow (public Q&A for anyone who codes)
303 
Stack Overflow (public Q&A for anyone who codes);Stack Overflow Jobs (for job seekers)
122 
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations)
 
36
Other values (20)
108 

Length

Max length305
Median length251
Mean length108.6672574
Min length37

Characters and Unicode

Total characters245262
Distinct characters39
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.3%

Sample

1st rowStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)
2nd rowStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)
3rd rowStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)
4th rowStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)
5th rowStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)

Common Values

ValueCountFrequency (%)
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)1021
45.2%
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)667
29.5%
Stack Overflow (public Q&A for anyone who codes)303
 
13.4%
Stack Overflow (public Q&A for anyone who codes);Stack Overflow Jobs (for job seekers)122
 
5.4%
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations)36
 
1.6%
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow for Teams (private Q&A for organizations)22
 
1.0%
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow Talent (for hiring companies/recruiters)18
 
0.8%
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations);Stack Overflow Talent (for hiring companies/recruiters);Stack Overflow Advertising (for technology companies)12
 
0.5%
Stack Overflow (public Q&A for anyone who codes);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations)8
 
0.4%
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Advertising (for technology companies)7
 
0.3%
Other values (15)41
 
1.8%

Length

2022-08-24T16:41:32.393179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
for5204
13.7%
q&a4143
10.9%
public4050
10.7%
overflow3309
 
8.7%
stack2253
 
5.9%
anyone2248
 
5.9%
who2248
 
5.9%
codes);stack1945
 
5.1%
of1806
 
4.8%
variety1802
 
4.8%
Other values (27)8920
23.5%

Most occurring characters

ValueCountFrequency (%)
35671
 
14.5%
o20963
 
8.5%
c15177
 
6.2%
e14580
 
5.9%
a13280
 
5.4%
r11632
 
4.7%
f10319
 
4.2%
t9077
 
3.7%
i8244
 
3.4%
l7441
 
3.0%
Other values (29)98878
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter172742
70.4%
Space Separator35671
 
14.5%
Uppercase Letter19573
 
8.0%
Other Punctuation7054
 
2.9%
Open Punctuation5111
 
2.1%
Close Punctuation5111
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o20963
 
12.1%
c15177
 
8.8%
e14580
 
8.4%
a13280
 
7.7%
r11632
 
6.7%
f10319
 
6.0%
t9077
 
5.3%
i8244
 
4.8%
l7441
 
4.3%
s7074
 
4.1%
Other values (15)54955
31.8%
Uppercase Letter
ValueCountFrequency (%)
S5111
26.1%
A4172
21.3%
Q4143
21.2%
O3309
16.9%
E1802
 
9.2%
J886
 
4.5%
T146
 
0.7%
I4
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
&4143
58.7%
;2858
40.5%
/53
 
0.8%
Space Separator
ValueCountFrequency (%)
35671
100.0%
Open Punctuation
ValueCountFrequency (%)
(5111
100.0%
Close Punctuation
ValueCountFrequency (%)
)5111
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin192315
78.4%
Common52947
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o20963
 
10.9%
c15177
 
7.9%
e14580
 
7.6%
a13280
 
6.9%
r11632
 
6.0%
f10319
 
5.4%
t9077
 
4.7%
i8244
 
4.3%
l7441
 
3.9%
s7074
 
3.7%
Other values (23)74528
38.8%
Common
ValueCountFrequency (%)
35671
67.4%
(5111
 
9.7%
)5111
 
9.7%
&4143
 
7.8%
;2858
 
5.4%
/53
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII245262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35671
 
14.5%
o20963
 
8.5%
c15177
 
6.2%
e14580
 
5.9%
a13280
 
5.4%
r11632
 
4.7%
f10319
 
4.2%
t9077
 
3.7%
i8244
 
3.4%
l7441
 
3.0%
Other values (29)98878
40.3%

new_stuck
Categorical

HIGH CARDINALITY
MISSING

Distinct215
Distinct (%)9.6%
Missing29
Missing (%)1.3%
Memory size17.8 KiB
Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later
 
107
Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back later
 
106
Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back later
 
98
Call a coworker or friend;Visit Stack Overflow;Watch help / tutorial videos
 
97
Call a coworker or friend;Visit Stack Overflow;Do other work and come back later
 
96
Other values (210)
1728 

Length

Max length225
Median length184
Mean length98.00224014
Min length20

Characters and Unicode

Total characters218741
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)3.3%

Sample

1st rowVisit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later
2nd rowCall a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back later
3rd rowCall a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later
4th rowVisit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Visit another developer community (please name):
5th rowPlay games;Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Visit another developer community (please name):

Common Values

ValueCountFrequency (%)
Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later107
 
4.7%
Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back later106
 
4.7%
Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back later98
 
4.3%
Call a coworker or friend;Visit Stack Overflow;Watch help / tutorial videos97
 
4.3%
Call a coworker or friend;Visit Stack Overflow;Do other work and come back later96
 
4.2%
Call a coworker or friend;Visit Stack Overflow;Watch help / tutorial videos;Do other work and come back later93
 
4.1%
Visit Stack Overflow;Watch help / tutorial videos92
 
4.1%
Visit Stack Overflow;Watch help / tutorial videos;Do other work and come back later82
 
3.6%
Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later80
 
3.5%
Call a coworker or friend;Visit Stack Overflow74
 
3.3%
Other values (205)1307
57.8%

Length

2022-08-24T16:41:32.580632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
other2502
 
7.5%
or2329
 
7.0%
a2329
 
7.0%
stack2095
 
6.3%
come1348
 
4.0%
work1348
 
4.0%
back1348
 
4.0%
and1348
 
4.0%
later1213
 
3.6%
help1181
 
3.5%
Other values (57)16443
49.1%

Most occurring characters

ValueCountFrequency (%)
31252
14.3%
o18701
 
8.5%
a17305
 
7.9%
r15956
 
7.3%
t15232
 
7.0%
e14581
 
6.7%
i12453
 
5.7%
l11224
 
5.1%
c9930
 
4.5%
k7120
 
3.3%
Other values (27)64987
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter167579
76.6%
Space Separator31252
 
14.3%
Uppercase Letter12220
 
5.6%
Other Punctuation7216
 
3.3%
Open Punctuation237
 
0.1%
Close Punctuation237
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o18701
11.2%
a17305
10.3%
r15956
9.5%
t15232
 
9.1%
e14581
 
8.7%
i12453
 
7.4%
l11224
 
6.7%
c9930
 
5.9%
k7120
 
4.2%
h6255
 
3.7%
Other values (12)38822
23.2%
Uppercase Letter
ValueCountFrequency (%)
V2332
19.1%
S2095
17.1%
O2095
17.1%
D1348
11.0%
W1181
9.7%
C1175
9.6%
G1154
9.4%
P525
 
4.3%
M315
 
2.6%
Other Punctuation
ValueCountFrequency (%)
;5798
80.3%
/1181
 
16.4%
:237
 
3.3%
Space Separator
ValueCountFrequency (%)
31252
100.0%
Open Punctuation
ValueCountFrequency (%)
(237
100.0%
Close Punctuation
ValueCountFrequency (%)
)237
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin179799
82.2%
Common38942
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o18701
 
10.4%
a17305
 
9.6%
r15956
 
8.9%
t15232
 
8.5%
e14581
 
8.1%
i12453
 
6.9%
l11224
 
6.2%
c9930
 
5.5%
k7120
 
4.0%
h6255
 
3.5%
Other values (21)51042
28.4%
Common
ValueCountFrequency (%)
31252
80.3%
;5798
 
14.9%
/1181
 
3.0%
(237
 
0.6%
)237
 
0.6%
:237
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII218741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31252
14.3%
o18701
 
8.5%
a17305
 
7.9%
r15956
 
7.3%
t15232
 
7.0%
e14581
 
6.7%
i12453
 
5.7%
l11224
 
5.1%
c9930
 
4.5%
k7120
 
3.3%
Other values (27)64987
29.7%

op_sys
Categorical

Distinct4
Distinct (%)0.2%
Missing16
Missing (%)0.7%
Memory size17.8 KiB
Windows
906 
Linux-based
814 
MacOS
524 
BSD
 
1

Length

Max length11
Median length7
Mean length7.981737194
Min length3

Characters and Unicode

Total characters17919
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowWindows
2nd rowLinux-based
3rd rowMacOS
4th rowWindows
5th rowWindows

Common Values

ValueCountFrequency (%)
Windows906
40.1%
Linux-based814
36.0%
MacOS524
23.2%
BSD1
 
< 0.1%
(Missing)16
 
0.7%

Length

2022-08-24T16:41:32.736898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:32.921801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
windows906
40.4%
linux-based814
36.3%
macos524
23.3%
bsd1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i1720
 
9.6%
n1720
 
9.6%
d1720
 
9.6%
s1720
 
9.6%
a1338
 
7.5%
W906
 
5.1%
o906
 
5.1%
w906
 
5.1%
e814
 
4.5%
b814
 
4.5%
Other values (10)5355
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13810
77.1%
Uppercase Letter3295
 
18.4%
Dash Punctuation814
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i1720
12.5%
n1720
12.5%
d1720
12.5%
s1720
12.5%
a1338
9.7%
o906
6.6%
w906
6.6%
e814
5.9%
b814
5.9%
x814
5.9%
Other values (2)1338
9.7%
Uppercase Letter
ValueCountFrequency (%)
W906
27.5%
L814
24.7%
S525
15.9%
M524
15.9%
O524
15.9%
B1
 
< 0.1%
D1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17105
95.5%
Common814
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i1720
 
10.1%
n1720
 
10.1%
d1720
 
10.1%
s1720
 
10.1%
a1338
 
7.8%
W906
 
5.3%
o906
 
5.3%
w906
 
5.3%
e814
 
4.8%
b814
 
4.8%
Other values (9)4541
26.5%
Common
ValueCountFrequency (%)
-814
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i1720
 
9.6%
n1720
 
9.6%
d1720
 
9.6%
s1720
 
9.6%
a1338
 
7.5%
W906
 
5.1%
o906
 
5.1%
w906
 
5.1%
e814
 
4.5%
b814
 
4.5%
Other values (10)5355
29.9%

org_size
Categorical

HIGH CORRELATION
MISSING

Distinct9
Distinct (%)0.4%
Missing38
Missing (%)1.7%
Memory size17.8 KiB
20 to 99 employees
444 
10,000 or more employees
400 
100 to 499 employees
355 
1,000 to 4,999 employees
292 
2 to 9 employees
213 
Other values (4)
519 

Length

Max length50
Median length24
Mean length21.64012596
Min length16

Characters and Unicode

Total characters48106
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1,000 to 4,999 employees
2nd row10 to 19 employees
3rd row20 to 99 employees
4th row5,000 to 9,999 employees
5th row2 to 9 employees

Common Values

ValueCountFrequency (%)
20 to 99 employees444
19.6%
10,000 or more employees400
17.7%
100 to 499 employees355
15.7%
1,000 to 4,999 employees292
12.9%
2 to 9 employees213
9.4%
10 to 19 employees160
 
7.1%
5,000 to 9,999 employees142
 
6.3%
500 to 999 employees138
 
6.1%
Just me - I am a freelancer, sole proprietor, etc.79
 
3.5%
(Missing)38
 
1.7%

Length

2022-08-24T16:41:33.093670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:33.265510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
employees2144
22.9%
to1744
18.6%
20444
 
4.7%
99444
 
4.7%
10,000400
 
4.3%
or400
 
4.3%
more400
 
4.3%
100355
 
3.8%
499355
 
3.8%
1,000292
 
3.1%
Other values (19)2388
25.5%

Most occurring characters

ValueCountFrequency (%)
e7385
15.4%
7143
14.8%
o4925
10.2%
04492
9.3%
93829
8.0%
m2702
 
5.6%
p2302
 
4.8%
l2302
 
4.8%
s2302
 
4.8%
y2144
 
4.5%
Other values (17)8580
17.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter27949
58.1%
Decimal Number11272
23.4%
Space Separator7143
 
14.8%
Other Punctuation1505
 
3.1%
Uppercase Letter158
 
0.3%
Dash Punctuation79
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e7385
26.4%
o4925
17.6%
m2702
 
9.7%
p2302
 
8.2%
l2302
 
8.2%
s2302
 
8.2%
y2144
 
7.7%
t1981
 
7.1%
r1195
 
4.3%
a237
 
0.8%
Other values (5)474
 
1.7%
Decimal Number
ValueCountFrequency (%)
04492
39.9%
93829
34.0%
11367
 
12.1%
2657
 
5.8%
4647
 
5.7%
5280
 
2.5%
Other Punctuation
ValueCountFrequency (%)
,1426
94.8%
.79
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
J79
50.0%
I79
50.0%
Space Separator
ValueCountFrequency (%)
7143
100.0%
Dash Punctuation
ValueCountFrequency (%)
-79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28107
58.4%
Common19999
41.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e7385
26.3%
o4925
17.5%
m2702
 
9.6%
p2302
 
8.2%
l2302
 
8.2%
s2302
 
8.2%
y2144
 
7.6%
t1981
 
7.0%
r1195
 
4.3%
a237
 
0.8%
Other values (7)632
 
2.2%
Common
ValueCountFrequency (%)
7143
35.7%
04492
22.5%
93829
19.1%
,1426
 
7.1%
11367
 
6.8%
2657
 
3.3%
4647
 
3.2%
5280
 
1.4%
-79
 
0.4%
.79
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII48106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e7385
15.4%
7143
14.8%
o4925
10.2%
04492
9.3%
93829
8.0%
m2702
 
5.6%
p2302
 
4.8%
l2302
 
4.8%
s2302
 
4.8%
y2144
 
4.5%
Other values (17)8580
17.8%

platform_desire_next_year
Categorical

HIGH CARDINALITY
MISSING

Distinct948
Distinct (%)45.9%
Missing196
Missing (%)8.7%
Memory size17.8 KiB
Linux
 
100
Docker;Linux
 
55
Linux;Windows
 
46
Windows
 
44
AWS;Docker;Kubernetes;Linux
 
29
Other values (943)
1791 

Length

Max length177
Median length111
Mean length38.56949153
Min length3

Characters and Unicode

Total characters79646
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique699 ?
Unique (%)33.8%

Sample

1st rowLinux;MacOS;Windows
2nd rowArduino;Docker;Linux;Raspberry Pi
3rd rowKubernetes;Linux
4th rowDocker;Kubernetes
5th rowDocker;Kubernetes

Common Values

ValueCountFrequency (%)
Linux100
 
4.4%
Docker;Linux55
 
2.4%
Linux;Windows46
 
2.0%
Windows44
 
1.9%
AWS;Docker;Kubernetes;Linux29
 
1.3%
Docker;Kubernetes;Linux27
 
1.2%
AWS;Docker;Linux24
 
1.1%
Linux;MacOS23
 
1.0%
AWS19
 
0.8%
Microsoft Azure;Windows16
 
0.7%
Other values (938)1682
74.4%
(Missing)196
 
8.7%

Length

2022-08-24T16:41:33.524657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cloud818
 
15.3%
pi322
 
6.0%
aws;docker;google203
 
3.8%
apps168
 
3.1%
and168
 
3.1%
pi;windows147
 
2.7%
azure145
 
2.7%
azure;windows136
 
2.5%
azure;raspberry131
 
2.4%
or119
 
2.2%
Other values (559)2993
55.9%

Most occurring characters

ValueCountFrequency (%)
o6944
 
8.7%
;6425
 
8.1%
r5923
 
7.4%
e5484
 
6.9%
i4114
 
5.2%
n4057
 
5.1%
u3885
 
4.9%
3285
 
4.1%
s3054
 
3.8%
d2731
 
3.4%
Other values (29)33744
42.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter55064
69.1%
Uppercase Letter14872
 
18.7%
Other Punctuation6425
 
8.1%
Space Separator3285
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o6944
12.6%
r5923
10.8%
e5484
 
10.0%
i4114
 
7.5%
n4057
 
7.4%
u3885
 
7.1%
s3054
 
5.5%
d2731
 
5.0%
a2389
 
4.3%
l2384
 
4.3%
Other values (12)14099
25.6%
Uppercase Letter
ValueCountFrequency (%)
A2187
14.7%
S1796
12.1%
W1783
12.0%
L1420
9.5%
P1367
9.2%
D1140
7.7%
M1078
7.2%
C818
 
5.5%
K725
 
4.9%
O711
 
4.8%
Other values (5)1847
12.4%
Other Punctuation
ValueCountFrequency (%)
;6425
100.0%
Space Separator
ValueCountFrequency (%)
3285
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin69936
87.8%
Common9710
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o6944
 
9.9%
r5923
 
8.5%
e5484
 
7.8%
i4114
 
5.9%
n4057
 
5.8%
u3885
 
5.6%
s3054
 
4.4%
d2731
 
3.9%
a2389
 
3.4%
l2384
 
3.4%
Other values (27)28971
41.4%
Common
ValueCountFrequency (%)
;6425
66.2%
3285
33.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII79646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o6944
 
8.7%
;6425
 
8.1%
r5923
 
7.4%
e5484
 
6.9%
i4114
 
5.2%
n4057
 
5.1%
u3885
 
4.9%
3285
 
4.1%
s3054
 
3.8%
d2731
 
3.4%
Other values (29)33744
42.4%

platform_worked_with
Categorical

HIGH CARDINALITY
MISSING

Distinct897
Distinct (%)41.3%
Missing91
Missing (%)4.0%
Memory size17.8 KiB
Windows
 
125
Linux;Windows
 
116
Linux
 
100
Docker;Linux
 
75
Docker;Linux;Windows
 
45
Other values (892)
1709 

Length

Max length166
Median length116
Mean length34.12626728
Min length3

Characters and Unicode

Total characters74054
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique656 ?
Unique (%)30.2%

Sample

1st rowMacOS;Windows
2nd rowArduino;AWS;Linux;Microsoft Azure;Raspberry Pi
3rd rowLinux;Microsoft Azure
4th rowDocker
5th rowDocker;Kubernetes;Linux;Raspberry Pi;Windows

Common Values

ValueCountFrequency (%)
Windows125
 
5.5%
Linux;Windows116
 
5.1%
Linux100
 
4.4%
Docker;Linux75
 
3.3%
Docker;Linux;Windows45
 
2.0%
Microsoft Azure;Windows28
 
1.2%
AWS;Docker;Linux25
 
1.1%
Linux;MacOS23
 
1.0%
AWS;Linux19
 
0.8%
Docker;Linux;MacOS19
 
0.8%
Other values (887)1595
70.5%
(Missing)91
 
4.0%

Length

2022-08-24T16:41:33.757291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cloud598
 
12.4%
apps184
 
3.8%
and184
 
3.8%
azure;windows174
 
3.6%
pi;windows153
 
3.2%
pi139
 
2.9%
aws;docker;google131
 
2.7%
windows125
 
2.6%
linux;windows116
 
2.4%
linux100
 
2.1%
Other values (574)2901
60.4%

Most occurring characters

ValueCountFrequency (%)
o6562
 
8.9%
;6217
 
8.4%
r5099
 
6.9%
i4506
 
6.1%
n4333
 
5.9%
e4095
 
5.5%
u3395
 
4.6%
s3226
 
4.4%
d3143
 
4.2%
2635
 
3.6%
Other values (29)30843
41.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter51028
68.9%
Uppercase Letter14174
 
19.1%
Other Punctuation6217
 
8.4%
Space Separator2635
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o6562
12.9%
r5099
10.0%
i4506
 
8.8%
n4333
 
8.5%
e4095
 
8.0%
u3395
 
6.7%
s3226
 
6.3%
d3143
 
6.2%
c2252
 
4.4%
a2158
 
4.2%
Other values (12)12259
24.0%
Uppercase Letter
ValueCountFrequency (%)
W2280
16.1%
A2031
14.3%
S1781
12.6%
L1591
11.2%
P1160
8.2%
M1087
7.7%
D1055
7.4%
O768
 
5.4%
C598
 
4.2%
G524
 
3.7%
Other values (5)1299
9.2%
Other Punctuation
ValueCountFrequency (%)
;6217
100.0%
Space Separator
ValueCountFrequency (%)
2635
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin65202
88.0%
Common8852
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o6562
 
10.1%
r5099
 
7.8%
i4506
 
6.9%
n4333
 
6.6%
e4095
 
6.3%
u3395
 
5.2%
s3226
 
4.9%
d3143
 
4.8%
W2280
 
3.5%
c2252
 
3.5%
Other values (27)26311
40.4%
Common
ValueCountFrequency (%)
;6217
70.2%
2635
29.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII74054
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o6562
 
8.9%
;6217
 
8.4%
r5099
 
6.9%
i4506
 
6.1%
n4333
 
5.9%
e4095
 
5.5%
u3395
 
4.6%
s3226
 
4.4%
d3143
 
4.2%
2635
 
3.6%
Other values (29)30843
41.6%

purchase_what
Categorical

MISSING

Distinct3
Distinct (%)0.1%
Missing208
Missing (%)9.2%
Memory size17.8 KiB
I have some influence
873 
I have little or no influence
733 
I have a great deal of influence
447 

Length

Max length32
Median length29
Mean length26.2513395
Min length21

Characters and Unicode

Total characters53894
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI have little or no influence
2nd rowI have some influence
3rd rowI have some influence
4th rowI have little or no influence
5th rowI have some influence

Common Values

ValueCountFrequency (%)
I have some influence873
38.6%
I have little or no influence733
32.4%
I have a great deal of influence447
19.8%
(Missing)208
 
9.2%

Length

2022-08-24T16:41:33.945667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:34.104337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
i2053
18.6%
have2053
18.6%
influence2053
18.6%
some873
7.9%
little733
 
6.7%
or733
 
6.7%
no733
 
6.7%
a447
 
4.1%
great447
 
4.1%
deal447
 
4.1%

Most occurring characters

ValueCountFrequency (%)
8966
16.6%
e8659
16.1%
n4839
9.0%
l3966
 
7.4%
a3394
 
6.3%
i2786
 
5.2%
o2786
 
5.2%
f2500
 
4.6%
c2053
 
3.8%
u2053
 
3.8%
Other values (9)11892
22.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter42875
79.6%
Space Separator8966
 
16.6%
Uppercase Letter2053
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8659
20.2%
n4839
11.3%
l3966
9.3%
a3394
 
7.9%
i2786
 
6.5%
o2786
 
6.5%
f2500
 
5.8%
c2053
 
4.8%
u2053
 
4.8%
v2053
 
4.8%
Other values (7)7786
18.2%
Space Separator
ValueCountFrequency (%)
8966
100.0%
Uppercase Letter
ValueCountFrequency (%)
I2053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin44928
83.4%
Common8966
 
16.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8659
19.3%
n4839
10.8%
l3966
8.8%
a3394
 
7.6%
i2786
 
6.2%
o2786
 
6.2%
f2500
 
5.6%
c2053
 
4.6%
u2053
 
4.6%
I2053
 
4.6%
Other values (8)9839
21.9%
Common
ValueCountFrequency (%)
8966
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII53894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8966
16.6%
e8659
16.1%
n4839
9.0%
l3966
 
7.4%
a3394
 
6.3%
i2786
 
5.2%
o2786
 
5.2%
f2500
 
4.6%
c2053
 
3.8%
u2053
 
3.8%
Other values (9)11892
22.1%

sexuality
Categorical

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)0.6%
Missing207
Missing (%)9.2%
Memory size17.8 KiB
Straight / Heterosexual
1862 
Bisexual
 
96
Gay or Lesbian
 
45
Bisexual;Straight / Heterosexual
 
14
Bisexual;Queer
 
11
Other values (7)
 
26

Length

Max length53
Median length23
Mean length22.09737098
Min length5

Characters and Unicode

Total characters45388
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowStraight / Heterosexual
2nd rowStraight / Heterosexual
3rd rowBisexual
4th rowStraight / Heterosexual
5th rowStraight / Heterosexual

Common Values

ValueCountFrequency (%)
Straight / Heterosexual1862
82.4%
Bisexual96
 
4.2%
Gay or Lesbian45
 
2.0%
Bisexual;Straight / Heterosexual14
 
0.6%
Bisexual;Queer11
 
0.5%
Queer10
 
0.4%
Straight / Heterosexual;Queer6
 
0.3%
Gay or Lesbian;Queer4
 
0.2%
Bisexual;Gay or Lesbian;Straight / Heterosexual;Queer3
 
0.1%
Bisexual;Gay or Lesbian;Straight / Heterosexual1
 
< 0.1%
Other values (2)2
 
0.1%
(Missing)207
 
9.2%

Length

2022-08-24T16:41:34.259719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1886
31.8%
heterosexual1877
31.6%
straight1868
31.5%
bisexual96
 
1.6%
or55
 
0.9%
gay49
 
0.8%
lesbian46
 
0.8%
bisexual;straight14
 
0.2%
bisexual;queer11
 
0.2%
queer10
 
0.2%
Other values (4)24
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e5910
13.0%
t5658
12.5%
a4009
 
8.8%
3882
 
8.6%
r3862
 
8.5%
s2068
 
4.6%
i2068
 
4.6%
u2048
 
4.5%
l2013
 
4.4%
x2013
 
4.4%
Other values (14)11857
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter35527
78.3%
Uppercase Letter4044
 
8.9%
Space Separator3882
 
8.6%
Other Punctuation1935
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5910
16.6%
t5658
15.9%
a4009
11.3%
r3862
10.9%
s2068
 
5.8%
i2068
 
5.8%
u2048
 
5.8%
l2013
 
5.7%
x2013
 
5.7%
o1941
 
5.5%
Other values (5)3937
11.1%
Uppercase Letter
ValueCountFrequency (%)
S1886
46.6%
H1886
46.6%
B127
 
3.1%
G55
 
1.4%
L55
 
1.4%
Q35
 
0.9%
Other Punctuation
ValueCountFrequency (%)
/1886
97.5%
;49
 
2.5%
Space Separator
ValueCountFrequency (%)
3882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39571
87.2%
Common5817
 
12.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5910
14.9%
t5658
14.3%
a4009
10.1%
r3862
9.8%
s2068
 
5.2%
i2068
 
5.2%
u2048
 
5.2%
l2013
 
5.1%
x2013
 
5.1%
o1941
 
4.9%
Other values (11)7981
20.2%
Common
ValueCountFrequency (%)
3882
66.7%
/1886
32.4%
;49
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII45388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e5910
13.0%
t5658
12.5%
a4009
 
8.8%
3882
 
8.6%
r3862
 
8.5%
s2068
 
4.6%
i2068
 
4.6%
u2048
 
4.5%
l2013
 
4.4%
x2013
 
4.4%
Other values (14)11857
26.1%

so_account
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing14
Missing (%)0.6%
Memory size17.8 KiB
Yes
1885 
No
237 
Not sure/can't remember
 
125

Length

Max length23
Median length3
Mean length4.007120605
Min length2

Characters and Unicode

Total characters9004
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowYes
4th rowNot sure/can't remember
5th rowYes

Common Values

ValueCountFrequency (%)
Yes1885
83.4%
No237
 
10.5%
Not sure/can't remember125
 
5.5%
(Missing)14
 
0.6%

Length

2022-08-24T16:41:34.415541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:34.555902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
yes1885
75.5%
no237
 
9.5%
not125
 
5.0%
sure/can't125
 
5.0%
remember125
 
5.0%

Most occurring characters

ValueCountFrequency (%)
e2385
26.5%
s2010
22.3%
Y1885
20.9%
r375
 
4.2%
N362
 
4.0%
o362
 
4.0%
t250
 
2.8%
250
 
2.8%
m250
 
2.8%
u125
 
1.4%
Other values (6)750
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6257
69.5%
Uppercase Letter2247
 
25.0%
Space Separator250
 
2.8%
Other Punctuation250
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2385
38.1%
s2010
32.1%
r375
 
6.0%
o362
 
5.8%
t250
 
4.0%
m250
 
4.0%
u125
 
2.0%
c125
 
2.0%
a125
 
2.0%
n125
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
Y1885
83.9%
N362
 
16.1%
Other Punctuation
ValueCountFrequency (%)
/125
50.0%
'125
50.0%
Space Separator
ValueCountFrequency (%)
250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8504
94.4%
Common500
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2385
28.0%
s2010
23.6%
Y1885
22.2%
r375
 
4.4%
N362
 
4.3%
o362
 
4.3%
t250
 
2.9%
m250
 
2.9%
u125
 
1.5%
c125
 
1.5%
Other values (3)375
 
4.4%
Common
ValueCountFrequency (%)
250
50.0%
/125
25.0%
'125
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2385
26.5%
s2010
22.3%
Y1885
20.9%
r375
 
4.2%
N362
 
4.0%
o362
 
4.0%
t250
 
2.8%
250
 
2.8%
m250
 
2.8%
u125
 
1.4%
Other values (6)750
 
8.3%

so_comm
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.3%
Missing13
Missing (%)0.6%
Memory size17.8 KiB
Yes, somewhat
744 
No, not really
567 
Neutral
399 
Yes, definitely
381 
No, not at all
140 

Length

Max length15
Median length14
Mean length12.55071174
Min length7

Characters and Unicode

Total characters28214
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes, somewhat
2nd rowYes, definitely
3rd rowYes, somewhat
4th rowNo, not really
5th rowYes, somewhat

Common Values

ValueCountFrequency (%)
Yes, somewhat744
32.9%
No, not really567
25.1%
Neutral399
17.6%
Yes, definitely381
16.9%
No, not at all140
 
6.2%
Not sure17
 
0.8%
(Missing)13
 
0.6%

Length

2022-08-24T16:41:34.696494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:34.852709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
yes1125
22.8%
somewhat744
15.0%
not724
14.6%
no707
14.3%
really567
11.5%
neutral399
 
8.1%
definitely381
 
7.7%
at140
 
2.8%
all140
 
2.8%
sure17
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e3614
12.8%
2696
 
9.6%
t2388
 
8.5%
l2194
 
7.8%
o2175
 
7.7%
a1990
 
7.1%
s1886
 
6.7%
,1832
 
6.5%
Y1125
 
4.0%
N1123
 
4.0%
Other values (10)7191
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21438
76.0%
Space Separator2696
 
9.6%
Uppercase Letter2248
 
8.0%
Other Punctuation1832
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3614
16.9%
t2388
11.1%
l2194
10.2%
o2175
10.1%
a1990
9.3%
s1886
8.8%
n1088
 
5.1%
r983
 
4.6%
y948
 
4.4%
i762
 
3.6%
Other values (6)3410
15.9%
Uppercase Letter
ValueCountFrequency (%)
Y1125
50.0%
N1123
50.0%
Space Separator
ValueCountFrequency (%)
2696
100.0%
Other Punctuation
ValueCountFrequency (%)
,1832
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23686
84.0%
Common4528
 
16.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3614
15.3%
t2388
10.1%
l2194
9.3%
o2175
9.2%
a1990
 
8.4%
s1886
 
8.0%
Y1125
 
4.7%
N1123
 
4.7%
n1088
 
4.6%
r983
 
4.2%
Other values (8)5120
21.6%
Common
ValueCountFrequency (%)
2696
59.5%
,1832
40.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII28214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3614
12.8%
2696
 
9.6%
t2388
 
8.5%
l2194
 
7.8%
o2175
 
7.7%
a1990
 
7.1%
s1886
 
6.7%
,1832
 
6.5%
Y1125
 
4.0%
N1123
 
4.0%
Other values (10)7191
25.5%

so_part_freq
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.3%
Missing377
Missing (%)16.7%
Memory size17.8 KiB
Less than once per month or monthly
785 
A few times per month or weekly
401 
I have never participated in Q&A on Stack Overflow
309 
A few times per week
212 
Daily or almost daily
113 

Length

Max length50
Median length35
Mean length33.6395966
Min length20

Characters and Unicode

Total characters63377
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLess than once per month or monthly
2nd rowA few times per week
3rd rowA few times per month or weekly
4th rowA few times per month or weekly
5th rowLess than once per month or monthly

Common Values

ValueCountFrequency (%)
Less than once per month or monthly785
34.7%
A few times per month or weekly401
17.7%
I have never participated in Q&A on Stack Overflow309
 
13.7%
A few times per week212
 
9.4%
Daily or almost daily113
 
5.0%
Multiple times per day64
 
2.8%
(Missing)377
16.7%

Length

2022-08-24T16:41:35.026206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:35.198042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
per1462
 
11.4%
or1299
 
10.1%
month1186
 
9.2%
less785
 
6.1%
once785
 
6.1%
monthly785
 
6.1%
than785
 
6.1%
times677
 
5.3%
a613
 
4.8%
few613
 
4.8%
Other values (15)3861
30.0%

Most occurring characters

ValueCountFrequency (%)
10967
17.3%
e7157
11.3%
o4786
 
7.6%
t4537
 
7.2%
n4468
 
7.0%
r3688
 
5.8%
h3065
 
4.8%
m2761
 
4.4%
a2424
 
3.8%
s2360
 
3.7%
Other values (20)17164
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter48981
77.3%
Space Separator10967
 
17.3%
Uppercase Letter3120
 
4.9%
Other Punctuation309
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e7157
14.6%
o4786
9.8%
t4537
 
9.3%
n4468
 
9.1%
r3688
 
7.5%
h3065
 
6.3%
m2761
 
5.6%
a2424
 
4.9%
s2360
 
4.8%
p2144
 
4.4%
Other values (10)11591
23.7%
Uppercase Letter
ValueCountFrequency (%)
A922
29.6%
L785
25.2%
I309
 
9.9%
Q309
 
9.9%
S309
 
9.9%
O309
 
9.9%
D113
 
3.6%
M64
 
2.1%
Space Separator
ValueCountFrequency (%)
10967
100.0%
Other Punctuation
ValueCountFrequency (%)
&309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin52101
82.2%
Common11276
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e7157
13.7%
o4786
 
9.2%
t4537
 
8.7%
n4468
 
8.6%
r3688
 
7.1%
h3065
 
5.9%
m2761
 
5.3%
a2424
 
4.7%
s2360
 
4.5%
p2144
 
4.1%
Other values (18)14711
28.2%
Common
ValueCountFrequency (%)
10967
97.3%
&309
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII63377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10967
17.3%
e7157
11.3%
o4786
 
7.6%
t4537
 
7.2%
n4468
 
7.0%
r3688
 
5.8%
h3065
 
4.8%
m2761
 
4.4%
a2424
 
3.8%
s2360
 
3.7%
Other values (20)17164
27.1%

so_visit_freq
Categorical

Distinct6
Distinct (%)0.3%
Missing13
Missing (%)0.6%
Memory size17.8 KiB
Multiple times per day
794 
Daily or almost daily
734 
A few times per week
479 
A few times per month or weekly
202 
Less than once per month or monthly
 
38

Length

Max length50
Median length35
Mean length22.28825623
Min length20

Characters and Unicode

Total characters50104
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMultiple times per day
2nd rowA few times per week
3rd rowA few times per week
4th rowMultiple times per day
5th rowDaily or almost daily

Common Values

ValueCountFrequency (%)
Multiple times per day794
35.1%
Daily or almost daily734
32.5%
A few times per week479
21.2%
A few times per month or weekly202
 
8.9%
Less than once per month or monthly38
 
1.7%
I have never visited Stack Overflow (before today)1
 
< 0.1%
(Missing)13
 
0.6%

Length

2022-08-24T16:41:35.403564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:35.559747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
per1513
14.8%
times1475
14.5%
daily1468
14.4%
or974
9.6%
multiple794
7.8%
day794
7.8%
almost734
7.2%
a681
6.7%
few681
6.7%
week479
 
4.7%
Other values (14)602
 
5.9%

Most occurring characters

ValueCountFrequency (%)
7947
15.9%
e5908
11.8%
l4031
 
8.0%
i3739
 
7.5%
t3322
 
6.6%
a3037
 
6.1%
y2503
 
5.0%
r2490
 
5.0%
m2487
 
5.0%
p2307
 
4.6%
Other values (21)12333
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter39905
79.6%
Space Separator7947
 
15.9%
Uppercase Letter2250
 
4.5%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5908
14.8%
l4031
10.1%
i3739
9.4%
t3322
8.3%
a3037
 
7.6%
y2503
 
6.3%
r2490
 
6.2%
m2487
 
6.2%
p2307
 
5.8%
s2286
 
5.7%
Other values (11)7795
19.5%
Uppercase Letter
ValueCountFrequency (%)
M794
35.3%
D734
32.6%
A681
30.3%
L38
 
1.7%
I1
 
< 0.1%
S1
 
< 0.1%
O1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
7947
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin42155
84.1%
Common7949
 
15.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5908
14.0%
l4031
9.6%
i3739
 
8.9%
t3322
 
7.9%
a3037
 
7.2%
y2503
 
5.9%
r2490
 
5.9%
m2487
 
5.9%
p2307
 
5.5%
s2286
 
5.4%
Other values (18)10045
23.8%
Common
ValueCountFrequency (%)
7947
> 99.9%
(1
 
< 0.1%
)1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII50104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7947
15.9%
e5908
11.8%
l4031
 
8.0%
i3739
 
7.5%
t3322
 
6.6%
a3037
 
6.1%
y2503
 
5.0%
r2490
 
5.0%
m2487
 
5.0%
p2307
 
4.6%
Other values (21)12333
24.6%

survey_ease
Categorical

Distinct3
Distinct (%)0.1%
Missing13
Missing (%)0.6%
Memory size17.8 KiB
Easy
1569 
Neither easy nor difficult
660 
Difficult
 
19

Length

Max length26
Median length4
Mean length10.50133452
Min length4

Characters and Unicode

Total characters23607
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEasy
2nd rowNeither easy nor difficult
3rd rowEasy
4th rowEasy
5th rowEasy

Common Values

ValueCountFrequency (%)
Easy1569
69.4%
Neither easy nor difficult660
29.2%
Difficult19
 
0.8%
(Missing)13
 
0.6%

Length

2022-08-24T16:41:35.747234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:35.891052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
easy2229
52.7%
difficult679
 
16.1%
neither660
 
15.6%
nor660
 
15.6%

Most occurring characters

ValueCountFrequency (%)
a2229
9.4%
s2229
9.4%
y2229
9.4%
i2018
 
8.5%
e1980
 
8.4%
1980
 
8.4%
E1569
 
6.6%
f1358
 
5.8%
t1339
 
5.7%
r1320
 
5.6%
Other values (9)5356
22.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19379
82.1%
Uppercase Letter2248
 
9.5%
Space Separator1980
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2229
11.5%
s2229
11.5%
y2229
11.5%
i2018
10.4%
e1980
10.2%
f1358
7.0%
t1339
6.9%
r1320
 
6.8%
c679
 
3.5%
u679
 
3.5%
Other values (5)3319
17.1%
Uppercase Letter
ValueCountFrequency (%)
E1569
69.8%
N660
29.4%
D19
 
0.8%
Space Separator
ValueCountFrequency (%)
1980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21627
91.6%
Common1980
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2229
10.3%
s2229
10.3%
y2229
10.3%
i2018
9.3%
e1980
9.2%
E1569
 
7.3%
f1358
 
6.3%
t1339
 
6.2%
r1320
 
6.1%
c679
 
3.1%
Other values (8)4677
21.6%
Common
ValueCountFrequency (%)
1980
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII23607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2229
9.4%
s2229
9.4%
y2229
9.4%
i2018
 
8.5%
e1980
 
8.4%
1980
 
8.4%
E1569
 
6.6%
f1358
 
5.8%
t1339
 
5.7%
r1320
 
5.6%
Other values (9)5356
22.7%

survey_length
Categorical

Distinct3
Distinct (%)0.1%
Missing10
Missing (%)0.4%
Memory size17.8 KiB
Appropriate in length
1756 
Too long
392 
Too short
 
103

Length

Max length21
Median length21
Mean length18.18702799
Min length8

Characters and Unicode

Total characters40939
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAppropriate in length
2nd rowAppropriate in length
3rd rowToo short
4th rowAppropriate in length
5th rowAppropriate in length

Common Values

ValueCountFrequency (%)
Appropriate in length1756
77.7%
Too long392
 
17.3%
Too short103
 
4.6%
(Missing)10
 
0.4%

Length

2022-08-24T16:41:36.031643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:36.172234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
appropriate1756
28.1%
in1756
28.1%
length1756
28.1%
too495
 
7.9%
long392
 
6.3%
short103
 
1.6%

Most occurring characters

ValueCountFrequency (%)
p5268
12.9%
4007
9.8%
n3904
9.5%
r3615
8.8%
t3615
8.8%
i3512
8.6%
e3512
8.6%
o3241
7.9%
l2148
 
5.2%
g2148
 
5.2%
Other values (5)5969
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter34681
84.7%
Space Separator4007
 
9.8%
Uppercase Letter2251
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p5268
15.2%
n3904
11.3%
r3615
10.4%
t3615
10.4%
i3512
10.1%
e3512
10.1%
o3241
9.3%
l2148
6.2%
g2148
6.2%
h1859
 
5.4%
Other values (2)1859
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A1756
78.0%
T495
 
22.0%
Space Separator
ValueCountFrequency (%)
4007
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin36932
90.2%
Common4007
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
p5268
14.3%
n3904
10.6%
r3615
9.8%
t3615
9.8%
i3512
9.5%
e3512
9.5%
o3241
8.8%
l2148
5.8%
g2148
5.8%
h1859
 
5.0%
Other values (4)4110
11.1%
Common
ValueCountFrequency (%)
4007
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII40939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p5268
12.9%
4007
9.8%
n3904
9.5%
r3615
8.8%
t3615
8.8%
i3512
8.6%
e3512
8.6%
o3241
7.9%
l2148
 
5.2%
g2148
 
5.2%
Other values (5)5969
14.6%

trans
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing62
Missing (%)2.7%
Memory size4.5 KiB
False
2180 
True
 
19
(Missing)
 
62
ValueCountFrequency (%)
False2180
96.4%
True19
 
0.8%
(Missing)62
 
2.7%
2022-08-24T16:41:36.311568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

undergrad_major
Categorical

MISSING

Distinct12
Distinct (%)0.6%
Missing110
Missing (%)4.9%
Memory size17.8 KiB
Computer science, computer engineering, or software engineering
1052 
Mathematics or statistics
286 
A natural science (such as biology, chemistry, physics, etc.)
264 
Another engineering discipline (such as civil, electrical, mechanical, etc.)
252 
Information systems, information technology, or system administration
 
80
Other values (7)
217 

Length

Max length78
Median length76
Mean length59.967457
Min length24

Characters and Unicode

Total characters128990
Distinct characters34
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComputer science, computer engineering, or software engineering
2nd rowA natural science (such as biology, chemistry, physics, etc.)
3rd rowComputer science, computer engineering, or software engineering
4th rowAnother engineering discipline (such as civil, electrical, mechanical, etc.)
5th rowMathematics or statistics

Common Values

ValueCountFrequency (%)
Computer science, computer engineering, or software engineering1052
46.5%
Mathematics or statistics286
 
12.6%
A natural science (such as biology, chemistry, physics, etc.)264
 
11.7%
Another engineering discipline (such as civil, electrical, mechanical, etc.)252
 
11.1%
Information systems, information technology, or system administration80
 
3.5%
A social science (such as anthropology, psychology, political science, etc.)74
 
3.3%
A business discipline (such as accounting, finance, marketing, etc.)64
 
2.8%
A humanities discipline (such as literature, history, philosophy, etc.)36
 
1.6%
A health science (such as nursing, pharmacy, radiology, etc.)17
 
0.8%
Web development or web design11
 
0.5%
Other values (2)15
 
0.7%
(Missing)110
 
4.9%

Length

2022-08-24T16:41:37.423237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
engineering2356
15.3%
computer2104
13.6%
science1481
 
9.6%
or1439
 
9.3%
software1052
 
6.8%
such717
 
4.6%
as717
 
4.6%
etc717
 
4.6%
a460
 
3.0%
discipline352
 
2.3%
Other values (45)4034
26.1%

Most occurring characters

ValueCountFrequency (%)
e16486
12.8%
13278
 
10.3%
i11150
 
8.6%
n10814
 
8.4%
c8696
 
6.7%
r8595
 
6.7%
t7383
 
5.7%
s7261
 
5.6%
o6695
 
5.2%
g5407
 
4.2%
Other values (24)33225
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter106995
82.9%
Space Separator13278
 
10.3%
Other Punctuation5132
 
4.0%
Uppercase Letter2151
 
1.7%
Open Punctuation717
 
0.6%
Close Punctuation717
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e16486
15.4%
i11150
10.4%
n10814
10.1%
c8696
8.1%
r8595
8.0%
t7383
 
6.9%
s7261
 
6.8%
o6695
 
6.3%
g5407
 
5.1%
a4840
 
4.5%
Other values (13)19668
18.4%
Uppercase Letter
ValueCountFrequency (%)
C1052
48.9%
A707
32.9%
M286
 
13.3%
I85
 
4.0%
W11
 
0.5%
F10
 
0.5%
Other Punctuation
ValueCountFrequency (%)
,4415
86.0%
.717
 
14.0%
Space Separator
ValueCountFrequency (%)
13278
100.0%
Open Punctuation
ValueCountFrequency (%)
(717
100.0%
Close Punctuation
ValueCountFrequency (%)
)717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin109146
84.6%
Common19844
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e16486
15.1%
i11150
10.2%
n10814
9.9%
c8696
 
8.0%
r8595
 
7.9%
t7383
 
6.8%
s7261
 
6.7%
o6695
 
6.1%
g5407
 
5.0%
a4840
 
4.4%
Other values (19)21819
20.0%
Common
ValueCountFrequency (%)
13278
66.9%
,4415
 
22.2%
(717
 
3.6%
.717
 
3.6%
)717
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII128990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e16486
12.8%
13278
 
10.3%
i11150
 
8.6%
n10814
 
8.4%
c8696
 
6.7%
r8595
 
6.7%
t7383
 
5.7%
s7261
 
5.6%
o6695
 
5.2%
g5407
 
4.2%
Other values (24)33225
25.8%

webframe_desire_next_year
Categorical

HIGH CARDINALITY
MISSING

Distinct435
Distinct (%)32.6%
Missing925
Missing (%)40.9%
Memory size17.8 KiB
Flask
145 
Django;Flask
 
89
React.js
 
82
Django
 
64
Vue.js
 
31
Other values (430)
925 

Length

Max length134
Median length91
Mean length20.0755988
Min length5

Characters and Unicode

Total characters26821
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique304 ?
Unique (%)22.8%

Sample

1st rowExpress;React.js
2nd rowFlask;Spring
3rd rowDjango;Flask
4th rowASP.NET Core
5th rowDjango;Express;Flask;jQuery;Laravel;React.js

Common Values

ValueCountFrequency (%)
Flask145
 
6.4%
Django;Flask89
 
3.9%
React.js82
 
3.6%
Django64
 
2.8%
Vue.js31
 
1.4%
ASP.NET Core27
 
1.2%
Flask;React.js26
 
1.1%
Django;Flask;React.js22
 
1.0%
Spring21
 
0.9%
jQuery19
 
0.8%
Other values (425)810
35.8%
(Missing)925
40.9%

Length

2022-08-24T16:41:37.628729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
flask145
 
8.4%
on92
 
5.3%
asp.net91
 
5.3%
django;flask89
 
5.2%
react.js82
 
4.8%
django64
 
3.7%
core52
 
3.0%
asp.net;asp.net45
 
2.6%
rails44
 
2.6%
vue.js31
 
1.8%
Other values (395)990
57.4%

Most occurring characters

ValueCountFrequency (%)
a2344
 
8.7%
;2167
 
8.1%
s2150
 
8.0%
j1732
 
6.5%
e1583
 
5.9%
.1335
 
5.0%
r1268
 
4.7%
l1190
 
4.4%
n1144
 
4.3%
u1078
 
4.0%
Other values (28)10830
40.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17585
65.6%
Uppercase Letter5345
 
19.9%
Other Punctuation3502
 
13.1%
Space Separator389
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2344
13.3%
s2150
12.2%
j1732
9.8%
e1583
9.0%
r1268
 
7.2%
l1190
 
6.8%
n1144
 
6.5%
u1078
 
6.1%
g1013
 
5.8%
o808
 
4.6%
Other values (11)3275
18.6%
Uppercase Letter
ValueCountFrequency (%)
R727
13.6%
A712
13.3%
F596
11.2%
E498
9.3%
D490
9.2%
S486
9.1%
V331
6.2%
N309
5.8%
P309
5.8%
T309
5.8%
Other values (4)578
10.8%
Other Punctuation
ValueCountFrequency (%)
;2167
61.9%
.1335
38.1%
Space Separator
ValueCountFrequency (%)
389
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22930
85.5%
Common3891
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2344
 
10.2%
s2150
 
9.4%
j1732
 
7.6%
e1583
 
6.9%
r1268
 
5.5%
l1190
 
5.2%
n1144
 
5.0%
u1078
 
4.7%
g1013
 
4.4%
o808
 
3.5%
Other values (25)8620
37.6%
Common
ValueCountFrequency (%)
;2167
55.7%
.1335
34.3%
389
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII26821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2344
 
8.7%
;2167
 
8.1%
s2150
 
8.0%
j1732
 
6.5%
e1583
 
5.9%
.1335
 
5.0%
r1268
 
4.7%
l1190
 
4.4%
n1144
 
4.3%
u1078
 
4.0%
Other values (28)10830
40.4%

webframe_worked_with
Categorical

HIGH CARDINALITY
MISSING

Distinct502
Distinct (%)34.6%
Missing809
Missing (%)35.8%
Memory size17.8 KiB
Flask
195 
Django
 
74
Django;Flask
 
57
React.js
 
53
jQuery
 
45
Other values (497)
1028 

Length

Max length134
Median length89
Mean length20.53787879
Min length5

Characters and Unicode

Total characters29821
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique350 ?
Unique (%)24.1%

Sample

1st rowExpress;React.js
2nd rowFlask;Spring
3rd rowDjango;Flask
4th rowExpress;Flask
5th rowASP.NET;ASP.NET Core;jQuery;Vue.js

Common Values

ValueCountFrequency (%)
Flask195
 
8.6%
Django74
 
3.3%
Django;Flask57
 
2.5%
React.js53
 
2.3%
jQuery45
 
2.0%
Flask;jQuery29
 
1.3%
Spring24
 
1.1%
Django;Flask;React.js23
 
1.0%
Flask;React.js22
 
1.0%
Django;jQuery21
 
0.9%
Other values (492)909
40.2%
(Missing)809
35.8%

Length

2022-08-24T16:41:37.834523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
flask195
 
10.7%
on83
 
4.6%
django74
 
4.1%
asp.net65
 
3.6%
asp.net;asp.net64
 
3.5%
django;flask57
 
3.1%
rails54
 
3.0%
react.js53
 
2.9%
jquery45
 
2.5%
angular;angular.js;asp.net;asp.net41
 
2.3%
Other values (466)1083
59.7%

Most occurring characters

ValueCountFrequency (%)
;2487
 
8.3%
a2405
 
8.1%
s2124
 
7.1%
j1812
 
6.1%
r1792
 
6.0%
e1724
 
5.8%
l1380
 
4.6%
u1336
 
4.5%
.1285
 
4.3%
n1228
 
4.1%
Other values (28)12248
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19344
64.9%
Uppercase Letter6343
 
21.3%
Other Punctuation3772
 
12.6%
Space Separator362
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2405
12.4%
s2124
11.0%
j1812
9.4%
r1792
9.3%
e1724
8.9%
l1380
 
7.1%
u1336
 
6.9%
n1228
 
6.3%
g1097
 
5.7%
y754
 
3.9%
Other values (11)3692
19.1%
Uppercase Letter
ValueCountFrequency (%)
A897
14.1%
S676
10.7%
F668
10.5%
E659
10.4%
R612
9.6%
Q530
8.4%
D467
7.4%
N425
6.7%
T425
6.7%
P425
6.7%
Other values (4)559
8.8%
Other Punctuation
ValueCountFrequency (%)
;2487
65.9%
.1285
34.1%
Space Separator
ValueCountFrequency (%)
362
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25687
86.1%
Common4134
 
13.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2405
 
9.4%
s2124
 
8.3%
j1812
 
7.1%
r1792
 
7.0%
e1724
 
6.7%
l1380
 
5.4%
u1336
 
5.2%
n1228
 
4.8%
g1097
 
4.3%
A897
 
3.5%
Other values (25)9892
38.5%
Common
ValueCountFrequency (%)
;2487
60.2%
.1285
31.1%
362
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII29821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
;2487
 
8.3%
a2405
 
8.1%
s2124
 
7.1%
j1812
 
6.1%
r1792
 
6.0%
e1724
 
5.8%
l1380
 
4.6%
u1336
 
4.5%
.1285
 
4.3%
n1228
 
4.1%
Other values (28)12248
41.1%

welcome_change
Categorical

MISSING

Distinct6
Distinct (%)0.3%
Missing62
Missing (%)2.7%
Memory size17.8 KiB
Just as welcome now as I felt last year
1605 
Somewhat more welcome now than last year
215 
Somewhat less welcome now than last year
 
144
A lot more welcome now than last year
 
103
A lot less welcome now than last year
 
86

Length

Max length55
Median length39
Mean length39.3260573
Min length37

Characters and Unicode

Total characters86478
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJust as welcome now as I felt last year
2nd rowJust as welcome now as I felt last year
3rd rowJust as welcome now as I felt last year
4th rowJust as welcome now as I felt last year
5th rowJust as welcome now as I felt last year

Common Values

ValueCountFrequency (%)
Just as welcome now as I felt last year1605
71.0%
Somewhat more welcome now than last year215
 
9.5%
Somewhat less welcome now than last year144
 
6.4%
A lot more welcome now than last year103
 
4.6%
A lot less welcome now than last year86
 
3.8%
Not applicable - I did not use Stack Overflow last year46
 
2.0%
(Missing)62
 
2.7%

Length

2022-08-24T16:41:38.011551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:38.203382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
as3210
16.9%
last2199
11.6%
year2199
11.6%
welcome2153
11.3%
now2153
11.3%
i1651
8.7%
just1605
8.5%
felt1605
8.5%
than548
 
2.9%
somewhat359
 
1.9%
Other values (11)1294
6.8%

Most occurring characters

ValueCountFrequency (%)
16777
19.4%
e9155
10.6%
a8653
10.0%
s7520
8.7%
t6643
 
7.7%
l6514
 
7.5%
o5310
 
6.1%
w4711
 
5.4%
m2830
 
3.3%
n2747
 
3.2%
Other values (19)15618
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter65713
76.0%
Space Separator16777
 
19.4%
Uppercase Letter3942
 
4.6%
Dash Punctuation46
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e9155
13.9%
a8653
13.2%
s7520
11.4%
t6643
10.1%
l6514
9.9%
o5310
8.1%
w4711
7.2%
m2830
 
4.3%
n2747
 
4.2%
r2563
 
3.9%
Other values (11)9067
13.8%
Uppercase Letter
ValueCountFrequency (%)
I1651
41.9%
J1605
40.7%
S405
 
10.3%
A189
 
4.8%
N46
 
1.2%
O46
 
1.2%
Space Separator
ValueCountFrequency (%)
16777
100.0%
Dash Punctuation
ValueCountFrequency (%)
-46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin69655
80.5%
Common16823
 
19.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e9155
13.1%
a8653
12.4%
s7520
10.8%
t6643
9.5%
l6514
9.4%
o5310
7.6%
w4711
 
6.8%
m2830
 
4.1%
n2747
 
3.9%
r2563
 
3.7%
Other values (17)13009
18.7%
Common
ValueCountFrequency (%)
16777
99.7%
-46
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII86478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16777
19.4%
e9155
10.6%
a8653
10.0%
s7520
8.7%
t6643
 
7.7%
l6514
 
7.5%
o5310
 
6.1%
w4711
 
5.4%
m2830
 
3.3%
n2747
 
3.2%
Other values (19)15618
18.1%

work_week_hrs
Real number (ℝ≥0)

MISSING

Distinct68
Distinct (%)3.1%
Missing42
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean41.58893646
Minimum4
Maximum375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-08-24T16:41:38.429380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile20
Q140
median40
Q345
95-th percentile60
Maximum375
Range371
Interquartile range (IQR)5

Descriptive statistics

Standard deviation14.58920078
Coefficient of variation (CV)0.3507952361
Kurtosis137.4217825
Mean41.58893646
Median Absolute Deviation (MAD)3
Skewness7.182683506
Sum92285.85
Variance212.8447795
MonotonicityNot monotonic
2022-08-24T16:41:38.585598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40899
39.8%
45241
 
10.7%
50211
 
9.3%
35115
 
5.1%
6075
 
3.3%
3871
 
3.1%
3070
 
3.1%
4255
 
2.4%
3743
 
1.9%
4834
 
1.5%
Other values (58)405
17.9%
(Missing)42
 
1.9%
ValueCountFrequency (%)
43
 
0.1%
54
 
0.2%
63
 
0.1%
74
 
0.2%
834
1.5%
913
 
0.6%
1010
 
0.4%
111
 
< 0.1%
122
 
0.1%
154
 
0.2%
ValueCountFrequency (%)
3751
 
< 0.1%
1682
0.1%
1604
0.2%
1501
 
< 0.1%
1201
 
< 0.1%
1003
0.1%
981
 
< 0.1%
903
0.1%
881
 
< 0.1%
841
 
< 0.1%

years_code
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.18487395
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-08-24T16:41:38.773054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median12
Q319
95-th percentile35
Maximum50
Range49
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.177664371
Coefficient of variation (CV)0.6470035902
Kurtosis1.269818701
Mean14.18487395
Median Absolute Deviation (MAD)5
Skewness1.241845184
Sum32072
Variance84.22952331
MonotonicityNot monotonic
2022-08-24T16:41:38.960819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
10213
 
9.4%
8144
 
6.4%
15143
 
6.3%
7142
 
6.3%
6135
 
6.0%
5129
 
5.7%
20117
 
5.2%
9111
 
4.9%
12108
 
4.8%
488
 
3.9%
Other values (38)931
41.2%
ValueCountFrequency (%)
18
 
0.4%
215
 
0.7%
356
 
2.5%
488
3.9%
5129
5.7%
6135
6.0%
7142
6.3%
8144
6.4%
9111
4.9%
10213
9.4%
ValueCountFrequency (%)
506
 
0.3%
481
 
< 0.1%
471
 
< 0.1%
456
 
0.3%
443
 
0.1%
435
 
0.2%
425
 
0.2%
414
 
0.2%
4021
0.9%
394
 
0.2%

years_code_pro
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct42
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.758513932
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2022-08-24T16:41:39.118130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q310
95-th percentile23
Maximum50
Range49
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.236954959
Coefficient of variation (CV)0.9327759185
Kurtosis3.551357021
Mean7.758513932
Median Absolute Deviation (MAD)3
Skewness1.764669474
Sum17542
Variance52.37351708
MonotonicityNot monotonic
2022-08-24T16:41:39.289965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3269
11.9%
1264
11.7%
2255
11.3%
5225
10.0%
4172
 
7.6%
6133
 
5.9%
10121
 
5.4%
8119
 
5.3%
7110
 
4.9%
1569
 
3.1%
Other values (32)524
23.2%
ValueCountFrequency (%)
1264
11.7%
2255
11.3%
3269
11.9%
4172
7.6%
5225
10.0%
6133
5.9%
7110
4.9%
8119
5.3%
962
 
2.7%
10121
5.4%
ValueCountFrequency (%)
502
 
0.1%
421
 
< 0.1%
411
 
< 0.1%
403
 
0.1%
382
 
0.1%
371
 
< 0.1%
362
 
0.1%
358
0.4%
342
 
0.1%
336
0.3%

age_cat
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
Under 30
1211 
At least 30
1050 

Length

Max length11
Median length8
Mean length9.393188854
Min length8

Characters and Unicode

Total characters21238
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAt least 30
2nd rowAt least 30
3rd rowUnder 30
4th rowAt least 30
5th rowUnder 30

Common Values

ValueCountFrequency (%)
Under 301211
53.6%
At least 301050
46.4%

Length

2022-08-24T16:41:39.455735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-24T16:41:39.589446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
302261
40.6%
under1211
21.7%
at1050
18.8%
least1050
18.8%

Most occurring characters

ValueCountFrequency (%)
3311
15.6%
e2261
10.6%
32261
10.6%
02261
10.6%
t2100
9.9%
U1211
 
5.7%
n1211
 
5.7%
d1211
 
5.7%
r1211
 
5.7%
A1050
 
4.9%
Other values (3)3150
14.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11144
52.5%
Decimal Number4522
21.3%
Space Separator3311
 
15.6%
Uppercase Letter2261
 
10.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2261
20.3%
t2100
18.8%
n1211
10.9%
d1211
10.9%
r1211
10.9%
l1050
9.4%
a1050
9.4%
s1050
9.4%
Decimal Number
ValueCountFrequency (%)
32261
50.0%
02261
50.0%
Uppercase Letter
ValueCountFrequency (%)
U1211
53.6%
A1050
46.4%
Space Separator
ValueCountFrequency (%)
3311
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13405
63.1%
Common7833
36.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2261
16.9%
t2100
15.7%
U1211
9.0%
n1211
9.0%
d1211
9.0%
r1211
9.0%
A1050
7.8%
l1050
7.8%
a1050
7.8%
s1050
7.8%
Common
ValueCountFrequency (%)
3311
42.3%
32261
28.9%
02261
28.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII21238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3311
15.6%
e2261
10.6%
32261
10.6%
02261
10.6%
t2100
9.9%
U1211
 
5.7%
n1211
 
5.7%
d1211
 
5.7%
r1211
 
5.7%
A1050
 
4.9%
Other values (3)3150
14.8%

Interactions

2022-08-24T16:41:06.566075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:53.443042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:55.818311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:57.847880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:00.265343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:02.158848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:03.874820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:05.265310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:12.868640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:53.876240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:55.990144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:58.043333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:00.459259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:02.330651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:04.034517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:05.421548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:13.041689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:54.173048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:56.227144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:58.237041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:00.638576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:02.511820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:04.272812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:05.562115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:13.245610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:54.443998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:56.514477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:58.640841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:00.868900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:02.697186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:04.449642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:05.736002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:13.399839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:54.774351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:56.855297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:59.367517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:01.031140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:02.869020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:04.618890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:05.907871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:13.604028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:54.961805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:57.151134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:59.650847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:01.218596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:03.056477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:04.793592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:06.064120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:13.764344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:55.164906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:57.405483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:59.846066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:01.441841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:03.228311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:04.949802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:06.268520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:13.917430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:55.505885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:40:57.678882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:00.039652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:02.002603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:03.594805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:05.106014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-24T16:41:06.409863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-24T16:41:39.683178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-24T16:41:39.901841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-24T16:41:40.153944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-24T16:41:40.450752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-24T16:41:40.968374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-24T16:41:14.574280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-24T16:41:16.614529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-24T16:41:19.081926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

respondentmain_branchhobbyistageage_1st_codeage_first_code_cutcomp_freqcomp_totalconverted_compcountrycurrency_desccurrency_symboldatabase_desire_next_yeardatabase_worked_withdev_typeed_levelemploymentethnicitygenderjob_factorsjob_satjob_seeklanguage_desire_next_yearlanguage_worked_withmisc_tech_desire_next_yearmisc_tech_worked_withnew_collab_tools_desire_next_yearnew_collab_tools_worked_withnew_dev_opsnew_dev_ops_imptnew_ed_imptnew_job_huntnew_job_hunt_researchnew_learnnew_off_topicnew_onboard_goodnew_other_commsnew_overtimenew_purchase_researchpurple_linknewso_sitesnew_stuckop_sysorg_sizeplatform_desire_next_yearplatform_worked_withpurchase_whatsexualityso_accountso_commso_part_freqso_visit_freqsurvey_easesurvey_lengthtransundergrad_majorwebframe_desire_next_yearwebframe_worked_withwelcome_changework_week_hrsyears_codeyears_code_proage_cat
036.0I am not primarily a developer, but I write code sometimes as part of my workYes34.030.0adultYearly60000.077556.0United KingdomPound sterlingGBPMicrosoft SQL Server;MongoDB;SQLiteIBM DB2;Microsoft SQL Server;MongoDB;SQLiteData or business analyst;Data scientist or machine learning specialistSome college/university study without earning a degreeEmployed full-timeWhite or of European descentManFlex time or a flexible schedule;Office environment or company culture;Opportunities for professional developmentSlightly satisfiedI’m not actively looking, but I am open to new opportunitiesC#;Go;HTML/CSS;JavaScript;Python;SQLC#;Go;HTML/CSS;Java;JavaScript;Python;R;SQLKeras;Node.js;Pandas;TensorFlowNode.js;PandasConfluence;Jira;Github;Slack;TrelloConfluence;Jira;Github;Slack;TrelloNot sureNeutralSomewhat importantHaving a bad day (or week or month) at work;Curious about other opportunities;Wanting to work with new technologies;Growth or leadership opportunitiesNoneEvery few monthsNoYesNoSometimes: 1-2 days per month but less than weeklyNoneHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back laterWindows1,000 to 4,999 employeesLinux;MacOS;WindowsMacOS;WindowsI have little or no influenceStraight / HeterosexualYesYes, somewhatLess than once per month or monthlyMultiple times per dayEasyAppropriate in lengthNoComputer science, computer engineering, or software engineeringExpress;React.jsExpress;React.jsJust as welcome now as I felt last year40.04.03.0At least 30
147.0I am a developer by professionYes53.010.0childYearly58000.074970.0United KingdomPound sterlingGBPPostgreSQL;SQLiteMicrosoft SQL Server;Oracle;PostgreSQL;SQLiteData scientist or machine learning specialist;Developer, back-end;Developer, QA or test;Engineer, data;ScientistOther doctoral degree (Ph.D., Ed.D., etc.)Employed full-timeWhite or of European descentManRemote work options;How widely used or impactful my work output would be;Opportunities for professional developmentVery satisfiedI’m not actively looking, but I am open to new opportunitiesBash/Shell/PowerShell;Java;Python;SQLBash/Shell/PowerShell;C#;Java;JavaScript;Python;Ruby;SQLPandas.NET;.NET CoreGithub;GitlabConfluence;Jira;Github;Gitlab;Microsoft Azure;Google Suite (Docs, Meet, etc)YesNeutralNot at all important/not necessaryJust because;Having a bad day (or week or month) at work;Curious about other opportunities;Wanting to work with new technologies;Looking to relocateRead company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Directly asking current or past employees at the companyOnce a yearNoOnboarding? What onboarding?YesOccasionally: 1-2 days per quarter but less than monthlyStart a free trial;Ask developers I know/work with;Visit developer communities like Stack OverflowHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back laterLinux-based10 to 19 employeesArduino;Docker;Linux;Raspberry PiArduino;AWS;Linux;Microsoft Azure;Raspberry PiI have some influenceStraight / HeterosexualYesYes, definitelyA few times per weekA few times per weekNeither easy nor difficultAppropriate in lengthNoA natural science (such as biology, chemistry, physics, etc.)Flask;SpringFlask;SpringJust as welcome now as I felt last year40.043.028.0At least 30
269.0I am a developer by professionYes25.012.0childYearly550000.0594539.0FranceEuropean EuroEURPostgreSQLMongoDBData scientist or machine learning specialist;Database administrator;Developer, back-end;Developer, full-stack;Engineer, dataMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)Employed full-timeWhite or of European descentManFlex time or a flexible schedule;How widely used or impactful my work output would be;Opportunities for professional developmentVery satisfiedI am not interested in new job opportunitiesPython;Rust;Scala;SQLHTML/CSS;PythonKeras;Pandas;TensorFlowKeras;Pandas;TensorFlowGithub;Slack;Google Suite (Docs, Meet, etc)Confluence;Jira;Github;Slack;Google Suite (Docs, Meet, etc)YesExtremely importantVery importantCurious about other opportunities;Better compensation;Trouble with leadership at my company;Wanting to work with new technologies;Growth or leadership opportunities;Looking to relocateRead company media, such as employee blogs or company culture videos;Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the companyOnce a yearNoNoNoSometimes: 1-2 days per month but less than weeklyAsk developers I know/work with;Visit developer communities like Stack OverflowHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back laterMacOS20 to 99 employeesKubernetes;LinuxLinux;Microsoft AzureI have some influenceBisexualYesYes, somewhatA few times per month or weeklyA few times per weekEasyToo shortNoComputer science, computer engineering, or software engineeringDjango;FlaskDjango;FlaskJust as welcome now as I felt last year40.013.03.0Under 30
3125.0I am not primarily a developer, but I write code sometimes as part of my workYes41.030.0adultMonthly200000.02000000.0United StatesUnited States dollarUSDPostgreSQLPostgreSQLData scientist or machine learning specialist;ScientistOther doctoral degree (Ph.D., Ed.D., etc.)Employed full-timeWhite or of European descentManFlex time or a flexible schedule;Family friendlinessVery satisfiedI am not interested in new job opportunitiesPythonPython;SQLKeras;Pandas;TensorFlow;Torch/PyTorchKeras;Pandas;TensorFlowJiraJiraNot sureNeutralCritically importantBetter work/life balancePersonal network - friends or family;Directly asking current or past employees at the companyOnce every few yearsNoYesNoOccasionally: 1-2 days per quarter but less than monthlyNoneAmusedStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Visit another developer community (please name):Windows5,000 to 9,999 employeesDocker;KubernetesDockerI have little or no influenceStraight / HeterosexualNot sure/can't rememberNo, not reallyNoneMultiple times per dayEasyAppropriate in lengthNoNoneNoneNoneJust as welcome now as I felt last year40.011.011.0At least 30
4147.0I am not primarily a developer, but I write code sometimes as part of my workNo28.015.0adultYearly50000.037816.0CanadaCanadian dollarCADNoneMariaDBData scientist or machine learning specialist;Database administrator;Designer;Developer, back-end;Developer, front-end;Developer, full-stackBachelor’s degree (B.A., B.S., B.Eng., etc.)Employed full-timeWhite or of European descentManDiversity of the company or organization;Flex time or a flexible schedule;Office environment or company cultureVery satisfiedI am not interested in new job opportunitiesRustHTML/CSS;JavaScript;Python;TypeScriptNoneNode.js;PandasNoneGithub;SlackNoSomewhat importantVery importantCurious about other opportunities;Wanting to work with new technologies;Growth or leadership opportunitiesRead company media, such as employee blogs or company culture videos;Personal network - friends or familyOnce a yearNoOnboarding? What onboarding?YesRarely: 1-2 days per year or lessStart a free trial;Ask developers I know/work with;Visit developer communities like Stack OverflowHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)Play games;Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Visit another developer community (please name):Windows2 to 9 employeesDocker;KubernetesNoneI have some influenceStraight / HeterosexualYesYes, somewhatA few times per month or weeklyDaily or almost dailyEasyAppropriate in lengthNoAnother engineering discipline (such as civil, electrical, mechanical, etc.)NoneExpress;FlaskJust as welcome now as I felt last year40.05.03.0Under 30
5152.0I am a developer by professionYes30.016.0adultMonthly10000.0121980.0SwitzerlandSwiss francCHFRedisRedisData scientist or machine learning specialist;Developer, back-end;Developer, front-end;Developer, full-stackMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)Employed full-timeWhite or of European descentManLanguages, frameworks, and other technologies I’d be working with;How widely used or impactful my work output would be;Office environment or company cultureVery satisfiedI’m not actively looking, but I am open to new opportunitiesBash/Shell/PowerShell;C#Bash/Shell/PowerShell;C#;HTML/CSS;JavaScript;Python;R.NET Core.NET;.NET CoreGitlabGithub;GitlabNoExtremely importantFairly importantNonePublicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the companyOnce a yearNoOnboarding? What onboarding?NoSometimes: 1-2 days per month but less than weeklyStart a free trial;Ask developers I know/work withHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)Visit Stack Overflow;Do other work and come back laterWindows2 to 9 employeesDocker;Kubernetes;Linux;Raspberry Pi;WindowsDocker;Kubernetes;Linux;Raspberry Pi;WindowsI have a great deal of influenceStraight / HeterosexualYesNo, not reallyLess than once per month or monthlyDaily or almost dailyEasyAppropriate in lengthNoMathematics or statisticsASP.NET CoreASP.NET;ASP.NET Core;jQuery;Vue.jsJust as welcome now as I felt last year40.014.06.0Under 30
6166.0I am a developer by professionYes28.09.0childYearly45000.048644.0GermanyEuropean EuroEURPostgreSQLElasticsearch;MongoDB;RedisAcademic researcher;Data or business analyst;Data scientist or machine learning specialist;Database administrator;Engineer, data;ScientistOther doctoral degree (Ph.D., Ed.D., etc.)Employed full-timeWhite or of European descentManSpecific department or team I’d be working on;Financial performance or funding status of the company or organization;Opportunities for professional developmentNeitherI am actively looking for a jobBash/Shell/PowerShell;C++;Python;SQLBash/Shell/PowerShell;PythonPandas;TensorFlow;Teraform;Torch/PyTorchPandasGithub;GitlabJira;Github;SlackNot sureSomewhat importantFairly importantHaving a bad day (or week or month) at work;Wanting to share accomplishments with a wider network;Curious about other opportunities;Better compensation;Better work/life balance;Wanting to work with new technologies;Growth or leadership opportunitiesRead company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase)Once every few yearsNot sureYesYesSometimes: 1-2 days per month but less than weeklyStart a free trial;Ask developers I know/work with;Visit developer communities like Stack OverflowHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)Visit Stack Overflow;PanicLinux-based2 to 9 employeesAWS;Google Cloud Platform;Linux;Raspberry PiAWS;Docker;LinuxI have a great deal of influenceStraight / HeterosexualYesYes, somewhatLess than once per month or monthlyMultiple times per dayNeither easy nor difficultAppropriate in lengthNoA natural science (such as biology, chemistry, physics, etc.)NoneNoneJust as welcome now as I felt last year40.015.06.0Under 30
7170.0I am a developer by professionYes26.017.0adultYearly130000.0130000.0United StatesUnited States dollarUSDMySQL;PostgreSQL;RedisElasticsearch;Firebase;MySQL;PostgreSQL;SQLiteData scientist or machine learning specialist;Database administrator;Developer, back-end;Developer, front-end;Developer, full-stack;Developer, game or graphics;Developer, mobile;Educator;Engineer, site reliability;Engineering manager;System administratorBachelor’s degree (B.A., B.S., B.Eng., etc.)Employed full-timeWhite or of European descentManLanguages, frameworks, and other technologies I’d be working with;Office environment or company culture;Opportunities for professional developmentVery satisfiedI am not interested in new job opportunitiesC#;HTML/CSS;JavaScript;PHP;Python;SQL;TypeScriptC#;HTML/CSS;JavaScript;PHP;Python;SQL.NET;Keras;Node.js;Pandas;React Native;TensorFlow;Torch/PyTorch;Unity 3D;Xamarin.NET;Keras;Node.js;Pandas;React Native;TensorFlow;Unity 3DJira;Github;Gitlab;Microsoft AzureGithub;Gitlab;Facebook WorkplaceYesExtremely importantNot at all important/not necessaryHaving a bad day (or week or month) at work;Wanting to share accomplishments with a wider network;Curious about other opportunities;Better compensation;Trouble with leadership at my company;Better work/life balance;Wanting to work with new technologies;Growth or leadership opportunities;Looking to relocateRead company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Read other media like news articles, founder profiles, etc. about the companyEvery few monthsNoYesNoSometimes: 1-2 days per month but less than weeklyStart a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2CrowdHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back laterLinux-based20 to 99 employeesAndroid;Arduino;AWS;Docker;Kubernetes;Linux;Raspberry Pi;WindowsAndroid;Arduino;AWS;Docker;Heroku;Linux;WindowsI have a great deal of influenceStraight / HeterosexualYesNo, not reallyI have never participated in Q&A on Stack OverflowDaily or almost dailyEasyAppropriate in lengthNoA humanities discipline (such as literature, history, philosophy, etc.)Django;Express;Flask;jQuery;Laravel;React.jsDjango;Express;Flask;jQuery;React.jsJust as welcome now as I felt last year45.04.02.0Under 30
8187.0I am a developer by professionYes43.012.0childYearly125000.094539.0CanadaCanadian dollarCADMongoDB;PostgreSQLMicrosoft SQL Server;MongoDBData scientist or machine learning specialist;Designer;Developer, back-end;Developer, desktop or enterprise applications;Developer, full-stackOther doctoral degree (Ph.D., Ed.D., etc.)Employed full-timeWhite or of European descentManIndustry that I’d be working in;Languages, frameworks, and other technologies I’d be working with;Office environment or company cultureSlightly satisfiedI’m not actively looking, but I am open to new opportunitiesJuliaPython;SQLPandasPandasConfluence;Jira;Github;Microsoft TeamsConfluence;Jira;Github;Microsoft TeamsYesExtremely importantFairly importantCurious about other opportunities;Better compensation;Growth or leadership opportunitiesRead company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind)Once a yearNot sureYesNoRarely: 1-2 days per year or lessStart a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2CrowdHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)Call a coworker or friend;Visit Stack OverflowWindows1,000 to 4,999 employeesGoogle Cloud Platform;WindowsAWS;Linux;WindowsI have some influenceStraight / HeterosexualYesNeutralLess than once per month or monthlyDaily or almost dailyEasyAppropriate in lengthNoA natural science (such as biology, chemistry, physics, etc.)Django;FlaskDjango;FlaskA lot less welcome now than last year37.530.018.0At least 30
9196.0I am a developer by professionYes23.015.0adultMonthly160000.01920000.0United StatesUnited States dollarUSDDynamoDB;Elasticsearch;MongoDB;MySQL;Oracle;PostgreSQL;RedisCassandra;Couchbase;Redis;SQLiteAcademic researcher;Data scientist or machine learning specialist;Designer;Developer, back-end;Developer, front-end;Developer, full-stack;DevOps specialistMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)Employed full-timeSouth AsianManLanguages, frameworks, and other technologies I’d be working with;Specific department or team I’d be working on;Opportunities for professional developmentVery satisfiedI’m not actively looking, but I am open to new opportunitiesGo;HTML/CSS;Java;JavaScript;PHP;Python;R;SQLAssembly;Bash/Shell/PowerShell;C;C++;ScalaApache Spark;Hadoop;Keras;Node.js;Pandas;TensorFlow;Teraform;Torch/PyTorchNoneGithub;Gitlab;Microsoft Azure;Google Suite (Docs, Meet, etc)JiraNoNeutralCritically importantCurious about other opportunities;Better compensation;Growth or leadership opportunitiesPersonal network - friends or family;Directly asking current or past employees at the companyEvery few monthsNoYesNoNeverAsk developers I know/work with;Visit developer communities like Stack OverflowAnnoyedStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)Visit Stack Overflow;Do other work and come back laterMacOS10,000 or more employeesAWS;Docker;Google Cloud Platform;Heroku;Kubernetes;Linux;MacOS;Microsoft Azure;WordPressAndroid;WindowsI have some influenceStraight / HeterosexualYesYes, definitelyMultiple times per dayMultiple times per dayEasyToo longNoComputer science, computer engineering, or software engineeringFlask;jQuery;Laravel;React.jsAngular;Angular.js;ASP.NET;ASP.NET CoreJust as welcome now as I felt last year40.08.03.0Under 30

Last rows

respondentmain_branchhobbyistageage_1st_codeage_first_code_cutcomp_freqcomp_totalconverted_compcountrycurrency_desccurrency_symboldatabase_desire_next_yeardatabase_worked_withdev_typeed_levelemploymentethnicitygenderjob_factorsjob_satjob_seeklanguage_desire_next_yearlanguage_worked_withmisc_tech_desire_next_yearmisc_tech_worked_withnew_collab_tools_desire_next_yearnew_collab_tools_worked_withnew_dev_opsnew_dev_ops_imptnew_ed_imptnew_job_huntnew_job_hunt_researchnew_learnnew_off_topicnew_onboard_goodnew_other_commsnew_overtimenew_purchase_researchpurple_linknewso_sitesnew_stuckop_sysorg_sizeplatform_desire_next_yearplatform_worked_withpurchase_whatsexualityso_accountso_commso_part_freqso_visit_freqsurvey_easesurvey_lengthtransundergrad_majorwebframe_desire_next_yearwebframe_worked_withwelcome_changework_week_hrsyears_codeyears_code_proage_cat
225162608.0I am a developer by professionYes30.019.0adultYearly155000.0155000.0United StatesUnited States dollarUSDPostgreSQLCassandra;Elasticsearch;MongoDB;PostgreSQL;RedisData scientist or machine learning specialist;Designer;Developer, back-end;Developer, desktop or enterprise applications;Developer, embedded applications or devices;Developer, front-end;Developer, full-stack;Developer, game or graphics;Developer, mobile;Developer, QA or test;DevOps specialist;Engineer, data;Engineering manager;Product manager;Scientist;Senior executive/VP;System administratorSome college/university study without earning a degreeEmployed full-timeWhite or of European descentManDiversity of the company or organization;Flex time or a flexible schedule;Remote work optionsVery satisfiedI am not interested in new job opportunitiesRust;SQL;Swift;TypeScriptBash/Shell/PowerShell;C;C++;Go;HTML/CSS;JavaScript;Python;Rust;SQL;Swift;TypeScriptKeras;TensorFlow;TeraformKeras;Node.js;TensorFlow;Teraform;Unity 3D;Unreal EngineGithub;Gitlab;SlackGithub;Gitlab;SlackNoNeutralSomewhat importantCurious about other opportunities;Better compensation;Better work/life balanceRead company media, such as employee blogs or company culture videos;Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Directly asking current or past employees at the companyNoneNoYesYesOften: 1-2 days per week or moreStart a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Research companies that have advertised on sites I visitHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)Meditate;Go for a walk or other physical activityMacOS20 to 99 employeesDocker;Google Cloud Platform;iOS;Kubernetes;Linux;MacOSAWS;Docker;Google Cloud Platform;iOS;Kubernetes;Linux;MacOSI have a great deal of influenceStraight / HeterosexualYesYes, somewhatLess than once per month or monthlyA few times per month or weeklyEasyAppropriate in lengthNoComputer science, computer engineering, or software engineeringNoneExpress;React.js;Vue.jsJust as welcome now as I felt last year40.010.010.0Under 30
225262616.0I am a developer by professionYes30.019.0adultYearly1560000.01560000.0United StatesUnited States dollarUSDPostgreSQLMicrosoft SQL Server;Oracle;PostgreSQLData scientist or machine learning specialistBachelor’s degree (B.A., B.S., B.Eng., etc.)Employed full-timeWhite or of European descentManFlex time or a flexible schedule;How widely used or impactful my work output would be;Office environment or company cultureNeitherI’m not actively looking, but I am open to new opportunitiesC++;Julia;Python;Rust;SQLC;C++;Python;Rust;SQLApache Spark;Hadoop;TensorFlow;Torch/PyTorchApache Spark;Hadoop;TensorFlow;Torch/PyTorchGithub;SlackConfluence;Github;SlackYesExtremely importantVery importantHaving a bad day (or week or month) at work;Curious about other opportunities;Trouble with leadership at my company;Wanting to work with new technologies;Growth or leadership opportunitiesRead company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Directly asking current or past employees at the companyOnce a yearNot sureOnboarding? What onboarding?YesOccasionally: 1-2 days per quarter but less than monthlyNoneHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)Meditate;Do other work and come back laterMacOS10,000 or more employeesAWS;Docker;Google Cloud Platform;Linux;Microsoft Azure;Raspberry PiDocker;Linux;Microsoft Azure;Raspberry Pi;WindowsI have little or no influenceStraight / HeterosexualNoNeutralNoneDaily or almost dailyEasyAppropriate in lengthNoMathematics or statisticsNoneNoneJust as welcome now as I felt last year45.011.05.0Under 30
225362617.0I am a developer by professionYes26.014.0adultMonthly10000.030372.0PolandPolish zlotyPLNNoneElasticsearch;RedisAcademic researcher;Data scientist or machine learning specialist;Developer, game or graphics;ScientistMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)Employed full-timeWhite or of European descentManSpecific department or team I’d be working on;Office environment or company culture;Opportunities for professional developmentVery satisfiedI’m not actively looking, but I am open to new opportunitiesPython;RustC++;Python;Rust;TypeScriptTensorFlow;Torch/PyTorchPandas;TensorFlow;Torch/PyTorchGithub;Gitlab;Microsoft TeamsConfluence;Github;Gitlab;Slack;Microsoft TeamsYesSomewhat importantVery importantWanting to share accomplishments with a wider network;Curious about other opportunities;Better compensation;Wanting to work with new technologiesRead company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the companyOnce a yearYesYesYesRarely: 1-2 days per year or lessStart a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Research companies that have advertised on sites I visitIndifferentStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)Call a coworker or friend;Visit Stack Overflow;Watch help / tutorial videos;Do other work and come back laterLinux-based20 to 99 employeesDocker;LinuxAndroid;Docker;Kubernetes;Linux;WindowsI have a great deal of influenceStraight / HeterosexualYesNeutralA few times per month or weeklyDaily or almost dailyNeither easy nor difficultToo longNoComputer science, computer engineering, or software engineeringReact.jsAngular;Flask;React.js;Vue.jsJust as welcome now as I felt last year40.010.05.0Under 30
225462705.0I am a developer by professionYes24.018.0adultYearly1800000.025131.0IndiaIndian rupeeINRMongoDB;MySQL;PostgreSQL;Redis;SQLiteMongoDB;MySQL;PostgreSQL;Redis;SQLiteAcademic researcher;Data or business analyst;Data scientist or machine learning specialist;Database administrator;Developer, back-end;Developer, desktop or enterprise applications;Developer, full-stack;Educator;Engineer, data;Engineer, site reliability;Scientist;System administratorBachelor’s degree (B.A., B.S., B.Eng., etc.)Employed full-timeEast AsianManFlex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Opportunities for professional developmentSlightly dissatisfiedI’m not actively looking, but I am open to new opportunitiesAssembly;Bash/Shell/PowerShell;C#;C++;Go;HTML/CSS;Java;JavaScript;Kotlin;Perl;Python;R;Ruby;Rust;SQL;TypeScriptAssembly;Bash/Shell/PowerShell;C;C++;HTML/CSS;Java;JavaScript;Python;R;Ruby;Rust;SQLAnsible;Apache Spark;Chef;Cordova;Hadoop;Keras;Node.js;Pandas;Puppet;React Native;TensorFlow;Teraform;Torch/PyTorchNode.js;Pandas;Puppet;TensorFlow;Torch/PyTorchConfluence;Jira;Github;Slack;Trello;Google Suite (Docs, Meet, etc)Confluence;Jira;Github;Slack;Trello;Google Suite (Docs, Meet, etc)NoExtremely importantVery importantCurious about other opportunities;Better compensation;Wanting to work with new technologies;Growth or leadership opportunitiesCompany reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the companyEvery few monthsYesYesYesRarely: 1-2 days per year or lessNoneAmusedStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations);Stack Overflow Talent (for hiring companies/recruiters);Stack Overflow Advertising (for technology companies)Visit Stack Overflow;Panic;Watch help / tutorial videos;Do other work and come back laterMacOS10,000 or more employeesAWS;Docker;Kubernetes;Linux;MacOS;Raspberry Pi;WindowsAWS;Docker;Kubernetes;Linux;MacOS;Raspberry Pi;WindowsI have little or no influenceStraight / HeterosexualYesNo, not at allLess than once per month or monthlyMultiple times per dayNeither easy nor difficultAppropriate in lengthNoComputer science, computer engineering, or software engineeringAngular;Angular.js;Django;Express;Flask;jQuery;Laravel;React.js;Ruby on Rails;Spring;Vue.jsDjango;Flask;jQuery;SpringJust as welcome now as I felt last year40.07.03.0Under 30
225562745.0I am a developer by professionYes24.014.0adultYearly50000.064630.0United KingdomPound sterlingGBPElasticsearch;PostgreSQL;SQLiteMongoDB;PostgreSQL;SQLiteData scientist or machine learning specialist;Developer, full-stackMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)Employed full-timeWhite or of European descentManIndustry that I’d be working in;Languages, frameworks, and other technologies I’d be working with;Remote work optionsSlightly satisfiedI’m not actively looking, but I am open to new opportunitiesBash/Shell/PowerShell;C++;Go;HTML/CSS;Python;RustBash/Shell/PowerShell;Go;HTML/CSS;JavaScript;Python;Rust;SQL;TypeScriptPandasNode.js;PandasGithub;Google Suite (Docs, Meet, etc)Jira;Github;Gitlab;Slack;Microsoft Azure;Trello;Google Suite (Docs, Meet, etc)NoNeutralFairly importantCurious about other opportunities;Better compensation;Trouble with my teammates;Wanting to work with new technologies;Growth or leadership opportunities;Looking to relocateRead company media, such as employee blogs or company culture videos;Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the companyEvery few monthsNoNoYesNeverAsk developers I know/work withAnnoyedStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Visit another developer community (please name):Linux-based20 to 99 employeesDocker;Linux;MacOSDocker;Google Cloud Platform;Linux;MacOS;Microsoft AzureI have a great deal of influenceStraight / HeterosexualYesNo, not reallyLess than once per month or monthlyMultiple times per dayEasyToo shortNoComputer science, computer engineering, or software engineeringFlask;Vue.jsAngular;Flask;jQuery;React.jsSomewhat more welcome now than last year30.08.02.0Under 30
225662812.0I am a developer by professionYes40.010.0childYearly145000.0145000.0United StatesUnited States dollarUSDCassandra;Elasticsearch;MariaDB;Microsoft SQL Server;MongoDB;MySQL;Oracle;PostgreSQL;RedisCassandra;MariaDB;Microsoft SQL Server;MongoDB;MySQL;Oracle;PostgreSQL;Redis;SQLiteData or business analyst;Data scientist or machine learning specialist;Database administrator;Designer;Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack;DevOps specialist;Educator;Engineer, data;Engineering manager;System administratorAssociate degree (A.A., A.S., etc.)Employed full-timeWhite or of European descentManFlex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Financial performance or funding status of the company or organizationVery satisfiedI am not interested in new job opportunitiesBash/Shell/PowerShell;HTML/CSS;JavaScript;Perl;Python;SQLBash/Shell/PowerShell;Go;HTML/CSS;JavaScript;Perl;PHP;Python;R;SQLAnsible;Apache Spark;Chef;Hadoop;Keras;Pandas;TensorFlow;Teraform;Torch/PyTorchAnsible;Apache Spark;Chef;Hadoop;Keras;Node.js;Pandas;Puppet;TensorFlow;Teraform;Torch/PyTorchConfluence;Jira;Github;Gitlab;Slack;Microsoft Teams;Microsoft Azure;Google Suite (Docs, Meet, etc)Confluence;Jira;Github;Gitlab;Google Suite (Docs, Meet, etc)YesExtremely importantFairly importantBetter compensation;Trouble with my direct manager;Better work/life balance;Growth or leadership opportunitiesRead company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the companyOnce every few yearsNoNoYesSometimes: 1-2 days per month but less than weeklyStart a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Research companies that have advertised on sites I visit;Research companies that have emailed meHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)Meditate;Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back laterLinux-based10,000 or more employeesAndroid;AWS;Docker;Google Cloud Platform;IBM Cloud or Watson;Kubernetes;Linux;Microsoft Azure;WordPressAndroid;AWS;Docker;Google Cloud Platform;IBM Cloud or Watson;Kubernetes;Linux;WordPressI have a great deal of influenceStraight / HeterosexualYesNo, not reallyLess than once per month or monthlyA few times per month or weeklyEasyToo longNoComputer science, computer engineering, or software engineeringFlask;jQueryAngular;Angular.js;Flask;jQuery;React.jsSomewhat less welcome now than last year50.030.020.0At least 30
225762835.0I am a developer by professionYes23.09.0childMonthly180000.033972.0Russian FederationRussian rubleRUBCassandra;PostgreSQL;RedisPostgreSQL;RedisData scientist or machine learning specialist;Developer, back-end;Engineering managerBachelor’s degree (B.A., B.S., B.Eng., etc.)Employed full-timeWhite or of European descentManIndustry that I’d be working in;Specific department or team I’d be working on;Opportunities for professional developmentVery satisfiedI am not interested in new job opportunitiesC#;Haskell;Rust;Scala;SQLBash/Shell/PowerShell;C;C#;C++;Go;Haskell;Kotlin;Python;Scala;SQL.NET Core;Apache Spark;Keras;TensorFlow;Torch/PyTorch.NET Core;Keras;TensorFlowGithub;Gitlab;Google Suite (Docs, Meet, etc)Confluence;Jira;Github;Gitlab;SlackYesExtremely importantSomewhat importantCurious about other opportunitiesRead company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the companyEvery few monthsNoYesYesOften: 1-2 days per week or moreStart a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2CrowdHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations)Call a coworker or friend;Visit Stack OverflowMacOS100 to 499 employeesDocker;KubernetesDocker;Kubernetes;Slack Apps and IntegrationsI have a great deal of influenceStraight / HeterosexualYesYes, somewhatI have never participated in Q&A on Stack OverflowA few times per month or weeklyEasyToo shortNoComputer science, computer engineering, or software engineeringASP.NET CoreASP.NET Core;FlaskJust as welcome now as I felt last year60.08.03.0Under 30
225862837.0I am a developer by professionYes27.08.0childMonthly7500.097284.0GermanyEuropean EuroEURNoneNoneData scientist or machine learning specialist;Developer, back-endMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)Employed full-timeWhite or of European descentManLanguages, frameworks, and other technologies I’d be working with;Specific department or team I’d be working on;Office environment or company cultureVery satisfiedI’m not actively looking, but I am open to new opportunitiesAssembly;Haskell;RustC++;Python;RustTorch/PyTorchNoneGithub;Google Suite (Docs, Meet, etc)Jira;Github;Slack;Google Suite (Docs, Meet, etc);Stack Overflow for TeamsYesSomewhat importantSomewhat importantNoneRead company media, such as employee blogs or company culture videos;Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the companyEvery few monthsNoYesNoneSometimes: 1-2 days per month but less than weeklyNoneHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations)Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back laterLinux-based500 to 999 employeesAWS;Docker;Kubernetes;LinuxAWS;Docker;LinuxI have little or no influenceStraight / HeterosexualYesYes, somewhatLess than once per month or monthlyDaily or almost dailyEasyAppropriate in lengthNoMathematics or statisticsNoneNoneJust as welcome now as I felt last year42.012.02.0Under 30
225962867.0I am not primarily a developer, but I write code sometimes as part of my workYes33.013.0childMonthly6000.072000.0PanamaUnited States dollarUSDNoneMariaDB;Microsoft SQL Server;MongoDB;SQLiteData or business analyst;Data scientist or machine learning specialist;Engineering managerMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)Employed full-timeHispanic or Latino/a/xManHow widely used or impactful my work output would be;Office environment or company culture;Opportunities for professional developmentVery satisfiedI’m not actively looking, but I am open to new opportunitiesPython;R;RubyJavaScript;Python;SQLReact Native;TensorFlowNode.js;PandasJira;Gitlab;SlackGithub;Microsoft Teams;TrelloNot sureExtremely importantSomewhat importantCurious about other opportunities;Better compensationRead company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the companyOnce every few yearsNot sureYesYesOften: 1-2 days per week or moreStart a free trial;Ask developers I know/work with;Research companies that have emailed meHello, old friendStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)Meditate;Play games;Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Panic;Watch help / tutorial videos;Do other work and come back later;Visit another developer community (please name):Windows10,000 or more employeesNoneAWS;Google Cloud Platform;WindowsI have some influenceStraight / HeterosexualYesNeutralI have never participated in Q&A on Stack OverflowDaily or almost dailyNeither easy nor difficultToo longNoAnother engineering discipline (such as civil, electrical, mechanical, etc.)NoneDjango;React.jsA lot less welcome now than last year45.015.02.0At least 30
226062882.0I am a developer by professionYes28.013.0childYearly180000.0180000.0United StatesUnited States dollarUSDCassandra;PostgreSQL;RedisDynamoDB;Elasticsearch;MariaDB;MongoDB;MySQL;Oracle;PostgreSQL;Redis;SQLiteData scientist or machine learning specialist;Developer, back-end;Developer, front-end;Developer, full-stackMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)Employed full-timeSouth AsianManLanguages, frameworks, and other technologies I’d be working with;Specific department or team I’d be working on;Financial performance or funding status of the company or organizationVery satisfiedI am not interested in new job opportunitiesJavaScript;Python;Scala;TypeScriptBash/Shell/PowerShell;C;C++;JavaScript;PythonApache Spark;Hadoop;Node.js;Pandas;Torch/PyTorchNoneConfluence;Github;Slack;Stack Overflow for TeamsConfluence;Jira;Github;Slack;Microsoft Teams;Stack Overflow for TeamsNoExtremely importantCritically importantBetter compensationRead company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind)Every few monthsNoneYesNoRarely: 1-2 days per year or lessStart a free trial;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd;Research companies that have advertised on sites I visitAnnoyedStack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow for Teams (private Q&A for organizations)Visit Stack Overflow;Watch help / tutorial videosMacOS10,000 or more employeesDocker;Heroku;Kubernetes;MacOS;Microsoft Azure;Slack Apps and IntegrationsAndroid;AWS;Docker;Heroku;IBM Cloud or Watson;Linux;MacOS;Slack Apps and Integrations;Windows;WordPressI have some influenceStraight / HeterosexualYesYes, somewhatA few times per month or weeklyDaily or almost dailyEasyAppropriate in lengthNoComputer science, computer engineering, or software engineeringAngular;Express;Flask;React.jsAngular;Angular.js;Django;Drupal;Express;FlaskJust as welcome now as I felt last year40.011.05.0Under 30